Systems and methods for replacing signal artifacts in a glucose sensor data stream

ABSTRACT

Systems and methods for minimizing or eliminating transient non-glucose related signal noise due to non-glucose rate limiting phenomenon such as interfering species, ischemia, pH changes, temperatures changes, known or unknown sources of mechanical, electrical and/or biochemical noise, and the like. The system monitors a data stream from a glucose sensor and detects signal artifacts that have higher amplitude than electronic or diffusion-related system noise. The system processes some or the entire data stream continually or intermittently based at least in part on whether the signal artifact event has occurred.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.11/498,410, filed Aug. 2, 2006, which is a continuation-in-part of U.S.application Ser. No. 10/648,849, filed Aug. 22, 2003, now U.S. Pat. No.8,010,174. U.S. application Ser. No. 11/498,410 is acontinuation-in-part of U.S. application Ser. No. 11/007,920, filed Dec.8, 2004, which claims the benefit of U.S. Provisional Application No.60/528,382 filed Dec. 9, 2003; U.S. Provisional Application 60/587,787;filed Jul. 13, 2004; and U.S. Provisional Application 60/614,683, filedSep. 30, 2004. U.S. application Ser. No. 11/498,410 is acontinuation-in-part of U.S. application Ser. No. 11/077,739, filed Mar.10, 2005, which claims the benefit of U.S. Provisional Application No.60/587,787 filed Jul. 13, 2004; U.S. Provisional Application No.60/587,800 filed Jul. 13, 2004; U.S. Provisional Application No.60/614,683 filed Sep. 30, 2004; and U.S. Provisional Application No.60/614,764 filed Sep. 30, 2004. Each of the aforementioned applicationsis incorporated by reference herein in its entirety, and each is herebyexpressly made a part of this specification.

FIELD OF THE INVENTION

The present invention relates generally to systems and methods forprocessing data received from a glucose sensor. Particularly, thepresent invention relates to systems and methods for detecting andprocessing signal artifacts, including detecting, estimating,predicting, filtering, displaying, and otherwise minimizing the effectsof signal artifacts in a glucose sensor data stream.

BACKGROUND OF THE INVENTION

Diabetes mellitus is a disorder in which the pancreas cannot createsufficient insulin (Type I or insulin dependent) and/or in which insulinis not effective (Type 2 or non-insulin dependent). In the diabeticstate, the victim suffers from high blood sugar, which causes an arrayof physiological derangements (kidney failure, skin ulcers, or bleedinginto the vitreous of the eye) associated with the deterioration of smallblood vessels. A hypoglycemic reaction (low blood sugar) is induced byan inadvertent overdose of insulin, or after a normal dose of insulin orglucose-lowering agent accompanied by extraordinary exercise orinsufficient food intake.

Conventionally, a diabetic person carries a self-monitoring bloodglucose (SMBG) monitor, which typically comprises uncomfortable fingerpricking methods. Due to the lack of comfort and convenience, a diabeticwill normally only measure his or her glucose level two to four timesper day. Unfortunately, these time intervals are so far spread apartthat the diabetic will likely find out too late, sometimes incurringdangerous side effects, of a hyperglycemic or hypoglycemic condition. Infact, it is not only unlikely that a diabetic will take a timely SMBGvalue, but additionally the diabetic will not know if their bloodglucose value is going up (higher) or down (lower) based on conventionalmethods.

Consequently, a variety of transdermal and implantable electrochemicalsensors are being developed for continuous detecting and/or quantifyingblood glucose values. Many implantable glucose sensors suffer fromcomplications within the body and provide only short-term andless-than-accurate sensing of blood glucose. Similarly, transdermalsensors have run into problems in accurately sensing and reporting backglucose values continuously over extended periods of time. Some effortshave been made to obtain blood glucose data from implantable devices andretrospectively determine blood glucose trends for analysis; howeverthese efforts do not aid the diabetic in determining real-time bloodglucose information. Some efforts have also been made to obtain bloodglucose data from transdermal devices for prospective data analysis,however similar problems have occurred.

Data streams from glucose sensors are known to have some amount ofnoise, caused by unwanted electronic and/or diffusion-related systemnoise that degrades the quality of the data stream. Some attempts havebeen made in conventional glucose sensors to smooth the raw output datastream representative of the concentration of blood glucose in thesample, for example by smoothing or filtering of Gaussian, white,random, and/or other relatively low amplitude noise in order to improvethe signal to noise ratio, and thus data output.

SUMMARY OF THE INVENTION

Systems and methods are provided that accurately detect signal noisethat is caused by substantially non-glucose reaction rate-limitingphenomena, such as interfering species, ischemia, pH changes,temperature changes, pressure, and stress, for example, which arereferred to herein as signal artifacts or “noise episodes”. Detectingsignal artifacts and processing the sensor data based on detection ofsignal artifacts provides accurate estimated glucose measurements to adiabetic patient so that they can proactively care for their conditionto safely avoid hyperglycemic and hypoglycemic conditions.

Accordingly, in a first aspect, a method of analyzing data from ananalyte sensor is provided, the method comprising receiving data fromthe analyte sensor, the data comprising at least one sensor data point;determining whether a signal artifact event has occurred; and processingthe received data, wherein the processing is based at least in part uponwhether the signal artifact event has occurred.

In an embodiment of the first aspect, the method further comprisesfiltering the received data to generate filtered data.

In an embodiment of the first aspect, determining whether a signalartifact has occurred comprises comparing the received data with thefiltered data to obtain at least one residual.

In an embodiment of the first aspect, a signal artifact event isdetermined to have occurred if the residual is exceeds a thresholdvalue.

In an embodiment of the first aspect, the method further comprisesdetermining whether another signal artifact event has occurred, whereinanother signal artifact event has occurred if the residual exceeds asecond threshold value.

In an embodiment of the first aspect, a signal artifact event isdetermined to have occurred if the residual is exceeds a threshold valuefor a predetermined period of time or for a predetermined amount ofdata.

In an embodiment of the first aspect, determining whether a signalartifact has occurred further comprises determining whether apredetermined number of residuals exceed a threshold over apredetermined period of time, or whether a predetermined amount of dataexceeds a threshold.

In an embodiment of the first aspect, determining whether a signalartifact event has occurred further comprises determining a differentialbetween a first residual at a first time point and a second residual ata second time point.

In an embodiment of the first aspect, determining whether a signalartifact event has occurred further comprises determining whether apredetermined number of differentials exceed a threshold over apredetermined period of time, or whether an amount of data exceeds athreshold.

In an embodiment of the first aspect, the method further comprisesreceiving reference data from a reference analyte monitor, the referencedata including at least one reference data point.

In an embodiment of the first aspect, processing the received datafurther comprises determining a reliability of the received data,wherein processing is conducted if the signal artifact event isdetermined to have not occurred.

In an embodiment of the first aspect, the method further comprisesmatching the reference data to substantially time corresponding receiveddata to form a matching data pair, wherein the reference data is matchedif the signal artifact event is determined to have not occurred.

In an embodiment of the first aspect, the method further comprisesincluding the reference data in a calibration factor for use incalibrating the glucose sensor, wherein the reference data is includedif the signal artifact event is determined to have not occurred.

In an embodiment of the first aspect, the method further comprisesprompting a user for a reference glucose value, wherein prompting isconducted if the signal artifact event is determined to have notoccurred.

In an embodiment of the first aspect, processing the received datacomprises displaying a graphical representation of the received data.

In an embodiment of the first aspect, processing the received datacomprises filtering the received data, wherein filtering is conducted ifthe signal artifact event is determined to have occurred.

In an embodiment of the first aspect, the method further comprisesfiltering the received data, wherein processing the received datacomprises displaying a graphical representation of the filtered data,wherein processing is conducted if the signal artifact event isdetermined to have occurred.

In an embodiment of the first aspect, the method further comprisesfiltering the received data to generate filtered data, whereindetermining whether a signal artifact event has occurred furthercomprises comparing the received data with the filtered data to obtain aresidual, and wherein processing the received data comprises utilizingthe residual to modify the filtered data.

In an embodiment of the first aspect, the method further comprisesfiltering the received data to generate filtered data, whereindetermining whether a signal artifact event has occurred furthercomprises comparing the received data with the filtered data to obtain aresidual and deriving a differential of the residual by calculating afirst derivative of the residual, and wherein processing the receiveddata comprises utilizing the differential to modify the filtered data.

In an embodiment of the first aspect, processing the received datacomprises compensating for a time lag.

In an embodiment of the first aspect, processing the received datacomprises displaying a graphical representation of the received data.

In an embodiment of the first aspect, the received data is an unfiltereddigital signal.

In an embodiment of the first aspect, processing the received datacomprises disabling display of a graphical representation of thereceived data, wherein processing is conducted if the signal artifactevent is determined to have occurred.

In an embodiment of the first aspect, processing the received datacomprises displaying a range of glucose values, wherein processing isconducted if the signal artifact event is determined to have occurred.

In an embodiment of the first aspect, processing the received datacomprises displaying a graphical indication of glucose trend, whereinprocessing is conducted if the signal artifact event is determined tohave occurred.

In an embodiment of the first aspect, processing the received datacomprises generating at least one estimated glucose value and displayinga graphical representation of the estimated glucose value, whereinprocessing is conducted if the signal artifact event is determined tohave occurred.

In an embodiment of the first aspect, processing the received datacomprises generating a confidence interval for at least one estimatedglucose value and displaying a graphical representation of theconfidence interval, wherein processing is conducted if the signalartifact event is determined to have occurred.

In a second aspect, a method for processing data from a glucose sensoris provided, the method comprising receiving data from the glucosesensor, the received data comprising at least one sensor data point;displaying a graphical representation of the data corresponding to atime period; and post-processing the displayed graphical representationof the data corresponding to the time period.

In an embodiment of the second aspect, post-processing is conductedperiodically.

In an embodiment of the second aspect, post-processing is conductedsubstantially continuously.

In an embodiment of the second aspect, the method further comprisesdetermining whether a signal artifact event has occurred and processingthe received data prior to the displaying step, wherein the processingis based at least in part upon whether the signal artifact event hasoccurred.

In an embodiment of the second aspect, post-processing comprisesfiltering the data to recalculate data corresponding to the time periodand displaying a graphical representation of the recalculated datacorresponding to the time period.

In an embodiment of the second aspect, the step of post-processingcomprises recalculating data corresponding to the time period, wherein atime lag induced by real-time filtering is substantially removed fromthe data corresponding to the time period; and displaying a graphicalrepresentation of the recalculated data corresponding to the timeperiod.

In an embodiment of the second aspect, recalculating the data comprisesalgorithmically smoothing at least one sensor data point over a movingwindow, wherein the moving window comprises time points before and afterthe sensor data point is obtained.

In an embodiment of the second aspect, the method further comprisesdisplaying a current glucose value representative of the most recentlyobtained sensor data point.

In a third aspect, a system configured to process data from an analytesensor is provided, the system comprising a data receiving moduleconfigured to receive sensor data from the analyte sensor, the datacomprising at least one sensor data point; a signal artifacts moduleconfigured to detect a signal artifact in the sensor data; and aprocessor module configured to process the sensor data, whereinprocessing is dependent at least in part upon whether the signalartifact is detected.

In an embodiment of the third aspect, the signal artifacts module isconfigured to compare raw sensor data with filtered sensor data todetermine a residual.

In an embodiment of the third aspect, the signal artifacts module isconfigured to detect a signal artifact if the residual exceeds athreshold value.

In an embodiment of the third aspect, the signal artifacts module isconfigured to detect a signal artifact if a predetermined number ofresiduals exceed a threshold value for a predetermined period of time orfor a predetermined amount of data.

In an embodiment of the third aspect, the signal artifacts module isconfigured to compare a first residual with a second signal residual todetermine a differential.

In an embodiment of the third aspect, the signal artifacts module isconfigured to detect a signal artifact if the differential exceeds athreshold value.

In an embodiment of the third aspect, the signal artifacts module isconfigured to detect a signal artifact if a predetermined number ofdifferentials exceed a threshold value for a predetermined period oftime or for a predetermined amount of data.

In an embodiment of the third aspect, the system further comprises areference data module configured to receive reference data from areference glucose monitor, the reference data comprising at least onereference data point.

In an embodiment of the third aspect, the signal artifacts module isconfigured to determine a reliability of the sensor data if the signalartifact is detected.

In an embodiment of the third aspect, the processor module is configuredto form at least one matched data pair by matching reference data tosubstantially time corresponding sensor data.

In an embodiment of the third aspect, the processor module is configuredto form a matching data pair if a signal artifact is not detected.

In an embodiment of the third aspect, the processor module is configuredto utilize the reference data for calibrating the glucose sensor if asignal artifact is not detected.

In an embodiment of the third aspect, the processor module is configuredto prompt a user for a reference glucose value if a signal artifact isnot detected.

In an embodiment of the third aspect, the data receiving module isconfigured to receive raw sensor data.

In an embodiment of the third aspect, the raw sensor data comprisesintegrated digital data.

In an embodiment of the third aspect, the processor module is configuredto display a graphical representation of the raw sensor data if a signalartifact is not detected.

In an embodiment of the third aspect, the data receiving module isconfigured to receive filtered sensor data.

In an embodiment of the third aspect, the processor module is configuredto display a graphical representation of the filtered sensor data if asignal artifact is detected.

In an embodiment of the third aspect, the processor module is configuredto filter the sensor data.

In an embodiment of the third aspect, the processor module is configuredto display a graphical representation of the filtered sensor data if asignal artifact is detected.

In an embodiment of the third aspect, the processor module is configuredto not display the sensor data if a signal artifact is detected.

In an embodiment of the third aspect, the processor module is configuredto display a range of glucose values if a signal artifact is detected.

In an embodiment of the third aspect, the processor module is configuredto display a directional indicator of glucose trend if a signal artifactis detected.

In an embodiment of the third aspect, the processor module is configuredto display at least one estimated glucose value if a signal artifact isdetected.

In an embodiment of the third aspect, the processor module is configuredto display a confidence interval for at least one estimated glucosevalue if a signal artifact is detected.

In a fourth aspect, a system configured to process data from an analytesensor is provided, the system comprising a data receiving moduleconfigured to receive sensor data from the analyte sensor, the datacomprising at least one sensor data point; an output module configuredto display a substantially real-time numerical value corresponding to amost recently received sensor data point and a graphical representationof sensor data corresponding to a time period; and a processor moduleconfigured to post-process the graphical representation of the datacorresponding to the time period, wherein the output module isconfigured to display the post-processed data.

In an embodiment of the fourth aspect, post-processing is conductedperiodically.

In an embodiment of the fourth aspect, post-processing is conductedsubstantially continuously.

In an embodiment of the fourth aspect, the processor module isconfigured to automatically post-process the graphical representation ofthe data corresponding to the time period.

In an embodiment of the fourth aspect, the processor module isconfigured to post-process the graphical representation of the datacorresponding to the time period responsive to a request.

In an embodiment of the fourth aspect, the output module is configuredto automatically display the post-processed graphical representation ofthe data corresponding to the time period.

In an embodiment of the fourth aspect, the output module is configuredto display the post-processed graphical representation of the datacorresponding to the time period responsive to a request.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is an exploded perspective view of a glucose sensor in oneembodiment.

FIG. 1B is side view of a distal portion of a transcutaneously insertedsensor in one embodiment.

FIG. 2 is a block diagram that illustrates sensor electronics in oneembodiment.

FIGS. 3A to 3D are schematic views of a receiver in first, second,third, and fourth embodiments, respectively.

FIG. 4A is a block diagram of receiver electronics in one embodiment.

FIG. 4B is an illustration of the receiver in one embodiment showing ananalyte trend graph, including measured analyte values, estimatedanalyte values, and a clinical risk zone.

FIG. 4C is an illustration of the receiver in another embodiment showinga representation of analyte concentration and directional trend using agradient bar.

FIG. 4D is an illustration of the receiver in yet another embodiment,including a screen that shows a numerical representation of the mostrecent measured analyte value.

FIG. 5 is a flow chart that illustrates the process of calibrating thesensor data in one embodiment.

FIG. 6 is a graph that illustrates a linear regression used to calibratethe sensor data in one embodiment.

FIG. 7A is a graph that shows a raw data stream obtained from a glucosesensor over a 4 hour time span in one example.

FIG. 7B is a graph that shows a raw data stream obtained from a glucosesensor over a 36 hour time span in another example.

FIG. 8 is a flow chart that illustrates the process of detecting andreplacing transient non-glucose related signal artifacts in a datastream in one embodiment.

FIG. 9 is a graph that illustrates the correlation between the counterelectrode voltage and signal artifacts in a data stream from a glucosesensor in one embodiment.

FIG. 10A is a circuit diagram of a potentiostat that controls a typicalthree-electrode system in one embodiment.

FIG. 10B is a diagram known as Cyclic-Voltammetry (CV) curve, whichillustrates the relationship between applied potential (V_(BIAS)) andsignal strength of the working electrode (I_(SENSE)) and can be used todetect signal artifacts.

FIG. 10C is a diagram showing a CV curve that illustrates an alternativeembodiment of signal artifacts detection, wherein pH and/or temperaturecan be monitoring using the CV curve.

FIG. 11 is a graph and spectrogram that illustrate the correlationbetween high frequency and signal artifacts observed by monitoring thefrequency content of a data stream in one embodiment.

FIG. 12 is a graph that illustrates a data stream obtained from aglucose sensor and a signal smoothed by trimmed linear regression thatcan be used to replace some of or the entire raw data stream in oneembodiment.

FIG. 13 is a graph that illustrates a data stream obtained from aglucose sensor and a FIR-smoothed data signal that can be used toreplace some of or the entire raw data stream in one embodiment.

FIG. 14 is a graph that illustrates a data stream obtained from aglucose sensor and an IIR-smoothed data signal that can be used toreplace some of or the entire raw data stream in one embodiment.

FIG. 15 is a flowchart that illustrates the process of selectivelyapplying signal estimation based on the severity of signal artifacts ona data stream.

FIG. 16 is a graph that illustrates selectively applying a signalestimation algorithm responsive to positive detection of signalartifacts on the raw data stream.

FIG. 17 is a graph that illustrates selectively applying a plurality ofsignal estimation algorithm factors responsive to a severity of signalartifacts on the raw data stream.

FIG. 18 is a flow chart that illustrates dynamic and intelligentestimation algorithm selection process in an alternative embodiment.

FIG. 19 is a graph that illustrates dynamic and intelligent estimationalgorithm selection applied to a data stream in one embodiment.

FIG. 20 is a flow chart that illustrates the process of dynamic andintelligent estimation and evaluation of analyte values in oneembodiment.

FIG. 21 is a graph that illustrates an evaluation of the selectedestimative algorithm in one embodiment.

FIG. 22 is a flow chart that illustrates the process of analyzing avariation of estimated future analyte value possibilities in oneembodiment.

FIG. 23 is a graph that illustrates variation analysis of estimatedglucose values in one embodiment.

FIG. 24 is a graph that illustrates variation of estimated analytevalues in another embodiment.

FIG. 25 is a flow chart that illustrates the process of estimating,measuring, and comparing analyte values in one embodiment.

FIG. 26 is a graph that illustrates comparison of estimated analytevalues in one embodiment.

FIG. 27 provides a flow chart that illustrates the evaluation ofreference and/or sensor data for statistical, clinical, and/orphysiological acceptability in one embodiment.

FIG. 28 is a flow chart that illustrates the evaluation of calibratedsensor data for aberrant values in one embodiment.

FIG. 29 provides a flow chart that illustrates a self-diagnostic ofsensor data in one embodiment.

FIG. 30 is a flow chart that illustrates the process of detecting andprocessing signal artifacts in certain embodiments.

FIG. 31 is a graph that illustrates a raw data stream from a glucosesensor for approximately 24 hours with a filtered version of the samedata stream superimposed on the same graph.

FIG. 32 is a flowchart that illustrates a method for processing datafrom a glucose sensor in certain embodiments.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description and examples illustrate some exemplaryembodiments of the disclosed invention in detail. Those of skill in theart will recognize that there are numerous variations and modificationsof this invention that are encompassed by its scope. Accordingly, thedescription of a certain exemplary embodiment should not be deemed tolimit the scope of the present invention.

Definitions

In order to facilitate an understanding of the preferred embodiments, anumber of terms are defined below.

The term “analyte” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a substance or chemicalconstituent in a biological fluid (for example, blood, interstitialfluid, cerebral spinal fluid, lymph fluid or urine) that can beanalyzed. Analytes can include naturally occurring substances,artificial substances, metabolites, and/or reaction products. In someembodiments, the analyte for measurement by the sensor heads, devices,and methods is analyte. However, other analytes are contemplated aswell, including but not limited to acarboxyprothrombin; acylcarnitine;adenine phosphoribosyl transferase; adenosine deaminase; albumin;alpha-fetoprotein; amino acid profiles (arginine (Krebs cycle),histidine/urocanic acid, homocysteine, phenylalanine/tyrosine,tryptophan); andrenostenedione; antipyrine; arabinitol enantiomers;arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactiveprotein; carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholicacid; chloroquine; cholesterol; cholinesterase; conjugated 1-βhydroxy-cholic acid; cortisol; creatine kinase; creatine kinase MMisoenzyme; cyclosporin A; d-penicillamine; de-ethylchloroquine;dehydroepiandrosterone sulfate; DNA (acetylator polymorphism, alcoholdehydrogenase, alpha 1-antitrypsin, cystic fibrosis, Duchenne/Beckermuscular dystrophy, analyte-6-phosphate dehydrogenase, hemoglobin A,hemoglobin S, hemoglobin C, hemoglobin D, hemoglobin E, hemoglobin F,D-Punjab, beta-thalassemia, hepatitis B virus, HCMV, HIV-1, HTLV-1,Leber hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax,sexual differentiation, 21-deoxycortisol); desbutylhalofantrine;dihydropteridine reductase; diptheria/tetanus antitoxin; erythrocytearginase; erythrocyte protoporphyrin; esterase D; fattyacids/acylglycines; free B-human chorionic gonadotropin; freeerythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine(FT3); fumarylacetoacetase; galactose/gal-1-phosphate;galactose-1-phosphate uridyltransferase; gentamicin; analyte-6-phosphatedehydrogenase; glutathione; glutathione perioxidase; glycocholic acid;glycosylated hemoglobin; halofantrine; hemoglobin variants;hexosaminidase A; human erythrocyte carbonic anhydrase I;17-alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase;immunoreactive trypsin; lactate; lead; lipoproteins ((a), B/A-1, β);lysozyme; mefloquine; netilmicin; phenobarbitone; phenytoin;phytanic/pristanic acid; progesterone; prolactin; prolidase; purinenucleoside phosphorylase; quinine; reverse tri-iodothyronine (rT3);selenium; serum pancreatic lipase; sissomicin; somatomedin C; specificantibodies (adenovirus, anti-nuclear antibody, anti-zeta antibody,arbovirus, Aujeszky's disease virus, dengue virus, Dracunculusmedinensis, Echinococcus granulosus, Entamoeba histolytica, enterovirus,Giardia duodenalisa, Helicobacter pylori, hepatitis B virus, herpesvirus, HIV-1, IgE (atopic disease), influenza virus, Leishmaniadonovani, leptospira, measles/mumps/rubella, Mycobacterium leprae,Mycoplasma pneumoniae, Myoglobin, Onchocerca volvulus, parainfluenzavirus, Plasmodium falciparum, poliovirus, Pseudomonas aeruginosa,respiratory syncytial virus, rickettsia (scrub typhus), Schistosomamansoni, Toxoplasma gondii, Trepenoma pallidium, Trypanosomacruzi/rangeli, vesicular stomatis virus, Wuchereria bancrofti, yellowfever virus); specific antigens (hepatitis B virus, HIV-1);succinylacetone; sulfadoxine; theophylline; thyrotropin (TSH); thyroxine(T4); thyroxine-binding globulin; trace elements; transferrin;UDP-galactose-4-epimerase; urea; uroporphyrinogen I synthase; vitamin A;white blood cells; and zinc protoporphyrin. Salts, sugar, protein, fat,vitamins, and hormones naturally occurring in blood or interstitialfluids can also constitute analytes in certain embodiments. The analytecan be naturally present in the biological fluid, for example, ametabolic product, a hormone, an antigen, an antibody, and the like.Alternatively, the analyte can be introduced into the body, for example,a contrast agent for imaging, a radioisotope, a chemical agent, afluorocarbon-based synthetic blood, or a drug or pharmaceuticalcomposition, including but not limited to insulin; ethanol; cannabis(marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide,amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine(crack cocaine); stimulants (amphetamines, methamphetamines, Ritalin,Cylert, Preludin, Didrex, PreState, Voranil, Sandrex, Plegine);depressants (barbituates, methaqualone, tranquilizers such as Valium,Librium, Miltown, Serax, Equanil, Tranxene); hallucinogens(phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics(heroin, codeine, morphine, opium, meperidine, Percocet, Percodan,Tussionex, Fentanyl, Darvon, Talwin, Lomotil); designer drugs (analogsof fentanyl, meperidine, amphetamines, methamphetamines, andphencyclidine, for example, Ecstasy); anabolic steroids; and nicotine.The metabolic products of drugs and pharmaceutical compositions are alsocontemplated analytes. Analytes such as neurochemicals and otherchemicals generated within the body can also be analyzed, such as, forexample, ascorbic acid, uric acid, dopamine, noradrenaline,3-methoxytyramine (3MT), 3,4-Dihydroxyphenylacetic acid (DOPAC),Homovanillic acid (HVA), 5-Hydroxytryptamine (5HT), and5-Hydroxyindoleacetic acid (FHIAA).

The term “EEPROM” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to electrically erasableprogrammable read-only memory, which is user-modifiable read-only memory(ROM) that can be erased and reprogrammed (e.g., written to) repeatedlythrough the application of higher than normal electrical voltage.

The term “SRAM” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to static random access memory(RAM) that retains data bits in its memory as long as power is beingsupplied.

The term “ROM” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to read-only memory, which is atype of data storage device manufactured with fixed contents. ROM isbroad enough to include EEPROM, for example, which is electricallyerasable programmable read-only memory (ROM).

The term “RAM” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a data storage device for whichthe order of access to different locations does not affect the speed ofaccess. RAM is broad enough to include SRAM, for example, which isstatic random access memory that retains data bits in its memory as longas power is being supplied.

The term “A/D Converter” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to hardware and/orsoftware that converts analog electrical signals into correspondingdigital signals.

The terms “microprocessor” and “processor” as used herein are broadterms and are to be given their ordinary and customary meaning to aperson of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto a computer system, state machine, and the like that performsarithmetic and logic operations using logic circuitry that responds toand processes the basic instructions that drive a computer.

The term “RF transceiver” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to a radio frequencytransmitter and/or receiver for transmitting and/or receiving signals.

The term “jitter” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to noise above and below the meancaused by ubiquitous noise caused by a circuit and/or environmentaleffects; jitter can be seen in amplitude, phase timing, or the width ofthe signal pulse.

The terms “raw data stream” and “data stream” as used herein are broadterms and are to be given their ordinary and customary meaning to aperson of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto an analog or digital signal directly related to the measured glucosefrom the glucose sensor. In one example, the raw data stream is digitaldata in “counts” converted by an A/D converter from an analog signal(e.g., voltage or amps) and includes one or more data pointsrepresentative of a glucose concentration. The terms broadly encompass aplurality of time spaced data points from a substantially continuousglucose sensor, which comprises individual measurements taken at timeintervals ranging from fractions of a second up to, e.g., 1, 2, or 5minutes or longer. In another example, the raw data stream includes anintegrated digital value, wherein the data includes one or more datapoints representative of the glucose sensor signal averaged over a timeperiod.

The term “calibration” as used herein is a broad term and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to the process of determining therelationship between the sensor data and the corresponding referencedata, which can be used to convert sensor data into meaningful valuessubstantially equivalent to the reference data. In some embodiments,namely, in continuous analyte sensors, calibration can be updated orrecalibrated over time as changes in the relationship between the sensordata and reference data occur, for example, due to changes insensitivity, baseline, transport, metabolism, and the like.

The terms “calibrated data” and “calibrated data stream” as used hereinare broad terms and are to be given their ordinary and customary meaningto a person of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto data that has been transformed from its raw state to another stateusing a function, for example a conversion function, to provide ameaningful value to a user.

The terms “smoothed data” and “filtered data” as used herein are broadterms and are to be given their ordinary and customary meaning to aperson of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto data that has been modified to make it smoother and more continuousand/or to remove or diminish outlying points, for example, by performinga moving average of the raw data stream. Examples of data filtersinclude FIR (finite impulse response), IIR (infinite impulse response),moving average filters, and the like.

The terms “smoothing” and “filtering” as used herein are broad terms andare to be given their ordinary and customary meaning to a person ofordinary skill in the art (and are not to be limited to a special orcustomized meaning), and furthermore refer without limitation tomodification of a set of data to make it smoother and more continuous orto remove or diminish outlying points, for example, by performing amoving average of the raw data stream.

The term “algorithm” as used herein is a broad term and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a computational process (forexample, programs) involved in transforming information from one stateto another, for example, by using computer processing.

The term “matched data pairs” as used herein is a broad term and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to reference data(for example, one or more reference analyte data points) matched withsubstantially time corresponding sensor data (for example, one or moresensor data points).

The term “counts” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a unit of measurement of adigital signal. In one example, a raw data stream measured in counts isdirectly related to a voltage (e.g., converted by an A/D converter),which is directly related to current from the working electrode. Inanother example, counter electrode voltage measured in counts isdirectly related to a voltage.

The term “sensor” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to the component or region of adevice by which an analyte can be quantified.

The term “needle” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a slender hollow instrument forintroducing material into or removing material from the body.

The terms “glucose sensor” and “member for determining the amount ofglucose in a biological sample,” as used herein, are broad terms and areused in an ordinary sense, including, without limitation, any mechanism(e.g., enzymatic or non-enzymatic) by which glucose can be quantified.For example, some embodiments utilize a membrane that contains glucoseoxidase that catalyzes the conversion of oxygen and glucose to hydrogenperoxide and gluconate, as illustrated by the following chemicalreaction:Glucose+O₂→Gluconate+H₂O₂

Because for each glucose molecule metabolized, there is a proportionalchange in the co-reactant O₂ and the product H₂O₂, one can use anelectrode to monitor the current change in either the co-reactant or theproduct to determine glucose concentration.

The terms “operably connected” and “operably linked” as used herein arebroad terms and are to be given their ordinary and customary meaning toa person of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto one or more components being linked to another component(s) in amanner that allows transmission of signals between the components. Forexample, one or more electrodes can be used to detect the amount ofglucose in a sample and convert that information into a signal, e.g., anelectrical or electromagnetic signal; the signal can then be transmittedto an electronic circuit. In this case, the electrode is “operablylinked” to the electronic circuitry. These terms are broad enough toinclude wireless connectivity.

The term “electronic circuitry” as used herein is a broad term and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to the components ofa device configured to process biological information obtained from ahost. In the case of a glucose-measuring device, the biologicalinformation is obtained by a sensor regarding a particular glucose in abiological fluid, thereby providing data regarding the amount of thatglucose in the fluid. U.S. Pat. Nos. 4,757,022, 5,497,772 and 4,787,398,which are hereby incorporated by reference, describe suitable electroniccircuits that can be utilized with devices including the biointerfacemembrane of a preferred embodiment.

The term “substantially” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to being largely butnot necessarily wholly that which is specified.

The term “proximal” as used herein is a broad term and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to near to a point of referencesuch as an origin, a point of attachment, or the midline of the body.For example, in some embodiments of a glucose sensor, wherein theglucose sensor is the point of reference, an oxygen sensor locatedproximal to the glucose sensor will be in contact with or nearby theglucose sensor such that their respective local environments are shared(e.g., levels of glucose, oxygen, pH, temperature, etc. are similar).

The term “distal” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to spaced relatively far from apoint of reference, such as an origin or a point of attachment, ormidline of the body. For example, in some embodiments of a glucosesensor, wherein the glucose sensor is the point of reference, an oxygensensor located distal to the glucose sensor will be sufficiently farfrom the glucose sensor such their respective local environments are notshared (e.g., levels of glucose, oxygen, pH, temperature, etc. may notbe similar).

The term “domain” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a region of the membrane systemthat can be a layer, a uniform or non-uniform gradient (for example, ananisotropic region of a membrane), or a portion of a membrane.

The terms “in vivo portion” and “distal portion” as used herein arebroad terms and are to be given their ordinary and customary meaning toa person of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto the portion of the device (for example, a sensor) adapted forinsertion into and/or existence within a living body of a host.

The terms “ex vivo portion” and “proximal portion” as used herein arebroad terms and are to be given their ordinary and customary meaning toa person of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto the portion of the device (for example, a sensor) adapted to remainand/or exist outside of a living body of a host.

The term “electrochemical cell” as used herein is a broad term and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to a device in whichchemical energy is converted to electrical energy. Such a cell typicallyconsists of two or more electrodes held apart from each other and incontact with an electrolyte solution. Connection of the electrodes to asource of direct electric current renders one of them negatively chargedand the other positively charged. Positive ions in the electrolytemigrate to the negative electrode (cathode) and there combine with oneor more electrons, losing part or all of their charge and becoming newions having lower charge or neutral atoms or molecules; at the sametime, negative ions migrate to the positive electrode (anode) andtransfer one or more electrons to it, also becoming new ions or neutralparticles. The overall effect of the two processes is the transfer ofelectrons from the negative ions to the positive ions, a chemicalreaction.

The term “potentiostat” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to an electricalsystem that controls the potential between the working and referenceelectrodes of a three-electrode cell at a preset value. It forceswhatever current is necessary to flow between the working and counterelectrodes to keep the desired potential, as long as the needed cellvoltage and current do not exceed the compliance limits of thepotentiostat.

The term “electrical potential” as used herein is a broad term and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to the electricalpotential difference between two points in a circuit which is the causeof the flow of a current.

The term “host” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to mammals, particularly humans.

The term “continuous analyte (or glucose) sensor” as used herein is abroad term and is to be given its ordinary and customary meaning to aperson of ordinary skill in the art (and is not to be limited to aspecial or customized meaning), and furthermore refers withoutlimitation to a device that continuously or continually measures aconcentration of an analyte, for example, at time intervals ranging fromfractions of a second up to, for example, 1, 2, or 5 minutes, or longer.In one exemplary embodiment, the continuous analyte sensor is a glucosesensor such as described in U.S. Pat. No. 6,001,067, which isincorporated herein by reference in its entirety.

The term “continuous analyte (or glucose) sensing” as used herein is abroad term and is to be given its ordinary and customary meaning to aperson of ordinary skill in the art (and is not to be limited to aspecial or customized meaning), and furthermore refers withoutlimitation to the period in which monitoring of an analyte iscontinuously or continually performed, for example, at time intervalsranging from fractions of a second up to, for example, 1, 2, or 5minutes, or longer.

The terms “reference analyte monitor,” “reference analyte meter,” and“reference analyte sensor” as used herein are broad terms and are to begiven their ordinary and customary meaning to a person of ordinary skillin the art (and are not to be limited to a special or customizedmeaning), and furthermore refer without limitation to a device thatmeasures a concentration of an analyte and can be used as a referencefor the continuous analyte sensor, for example a self-monitoring bloodglucose meter (SMBG) can be used as a reference for a continuous glucosesensor for comparison, calibration, and the like.

The terms “sensor head” and “sensing region” as used herein are broadterms and are to be given their ordinary and customary meaning to aperson of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto the region of a monitoring device responsible for the detection of aparticular analyte. The sensing region generally comprises anon-conductive body, a working electrode (anode), a reference electrode(optional), and/or a counter electrode (cathode) passing through andsecured within the body forming electrochemically reactive surfaces onthe body and an electronic connective means at another location on thebody, and a multi-domain membrane affixed to the body and covering theelectrochemically reactive surface.

The term “electrochemically reactive surface” as used herein is a broadterm and is to be given its ordinary and customary meaning to a personof ordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation to thesurface of an electrode where an electrochemical reaction takes place.In the case of the working electrode, the hydrogen peroxide produced bythe enzyme catalyzed reaction of the glucose being detected reactscreating a measurable electronic current (e.g., detection of glucoseutilizing glucose oxidase produces H₂O₂ as a by product, H₂O₂ reactswith the surface of the working electrode producing two protons (2H⁺),two electrons (2e⁻) and one molecule of oxygen (O₂) which produces theelectronic current being detected). In the case of the counterelectrode, a reducible species, e.g., O₂ is reduced at the electrodesurface in order to balance the current being generated by the workingelectrode.

The term “electronic connection” as used herein is a broad term and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to any electronicconnection known to those in the art that can be utilized to interfacethe sensor head electrodes with the electronic circuitry of a devicesuch as mechanical (e.g., pin and socket) or soldered.

The term “sensing membrane” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to a permeable orsemi-permeable membrane that can be comprised of two or more domains andis typically constructed of materials of a few microns thickness ormore, which are permeable to oxygen and may or may not be permeable toglucose. In one example, the sensing membrane comprises an immobilizedglucose oxidase enzyme, which enables an electrochemical reaction tooccur to measure a concentration of glucose.

The term “biointerface membrane” as used herein is a broad term and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to a permeablemembrane that can be comprised of two or more domains and is typicallyconstructed of materials of a few microns thickness or more, which canbe placed over the sensor body to keep host cells (e.g., macrophages)from gaining proximity to, and thereby damaging, the sensing membrane orforming a barrier cell layer and interfering with the transport ofglucose across the tissue-device interface.

The term “Clarke Error Grid” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to an error gridanalysis, which evaluates the clinical significance of the differencebetween a reference glucose value and a sensor generated glucose value,taking into account 1) the value of the reference glucose measurement,2) the value of the sensor glucose measurement, 3) the relativedifference between the two values, and 4) the clinical significance ofthis difference. See Clarke et al., “Evaluating Clinical Accuracy ofSystems for Self-Monitoring of Blood Glucose,” Diabetes Care, Volume 10,Number 5, September-October 1987, which is incorporated by referenceherein in its entirety.

The term “physiologically feasible” as used herein is a broad term andis to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation to thephysiological parameters obtained from continuous studies of glucosedata in humans and/or animals. For example, a maximal sustained rate ofchange of glucose in humans of about 4 to 5 mg/dL/min and a maximumacceleration of the rate of change of about 0.1 to 0.2 mg/dL/min/min aredeemed physiologically feasible limits. Values outside of these limitswould be considered non-physiological and likely a result of signalerror, for example. As another example, the rate of change of glucose islowest at the maxima and minima of the daily glucose range, which arethe areas of greatest risk in patient treatment, thus a physiologicallyfeasible rate of change can be set at the maxima and minima based oncontinuous studies of glucose data. As a further example, it has beenobserved that the best solution for the shape of the curve at any pointalong glucose signal data stream over a certain time period (e.g., about20 to 30 minutes) is a straight line, which can be used to setphysiological limits.

The term “ischemia” as used herein is a broad term and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to local and temporary deficiencyof blood supply due to obstruction of circulation to a part (e.g.,sensor). Ischemia can be caused by mechanical obstruction (e.g.,arterial narrowing or disruption) of the blood supply, for example.

The term “system noise” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to unwantedelectronic or diffusion-related noise which can include Gaussian,motion-related, flicker, kinetic, or other white noise, for example.

The terms “noise,” “noise event(s),” “noise episode(s),” “signalartifact(s),” “signal artifact event(s),” and “signal artifactepisode(s)” as used herein are broad terms and are to be given theirordinary and customary meaning to a person of ordinary skill in the art(and are not to be limited to a special or customized meaning), andfurthermore refer without limitation to signal noise that is caused bysubstantially non-glucose related, such as interfering species, macro-or micro-motion, ischemia, pH changes, temperature changes, pressure,stress, or even unknown sources of mechanical, electrical and/orbiochemical noise for example. In some embodiments, signal artifacts aretransient and characterized by a higher amplitude than system noise, anddescribed as “transient non-glucose related signal artifact(s) that havea higher amplitude than system noise.” In some embodiments, noise iscaused by rate-limiting (or rate-increasing) phenomena. In somecircumstances, the source of the noise is unknown.

The terms “low noise” as used herein is a broad term and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to noise that substantiallydecreases signal amplitude.

The terms “high noise” and “high spikes” as used herein are broad termsand are to be given their ordinary and customary meaning to a person ofordinary skill in the art (and are not to be limited to a special orcustomized meaning), and furthermore refer without limitation to noisethat substantially increases signal amplitude.

The term “frequency content” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to the spectraldensity, including the frequencies contained within a signal and theirpower.

The term “spectral density” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to power spectraldensity of a given bandwidth of electromagnetic radiation is the totalpower in this bandwidth divided by the specified bandwidth. Spectraldensity is usually expressed in Watts per Hertz (W/Hz).

The term “orthogonal transform” as used herein is a broad term and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to a generalintegral transform that is defined by

g(α) = ∫_(a)^(b)f(t)K(α, t)𝕕t, where  K(α, t)represents a set of orthogonal basis functions.

The term “Fourier Transform” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to a technique forexpressing a waveform as a weighted sum of sines and cosines.

The term “Discrete Fourier Transform” as used herein is a broad term andis to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation to aspecialized Fourier transform where the variables are discrete.

The term “wavelet transform” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to a transform whichconverts a signal into a series of wavelets, which in theory allowssignals processed by the wavelet transform to be stored more efficientlythan ones processed by Fourier transform. Wavelets can also beconstructed with rough edges, to better approximate real-world signals.

The term “chronoamperometry” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to anelectrochemical measuring technique used for electrochemical analysis orfor the determination of the kinetics and mechanism of electrodereactions. A fast-rising potential pulse is enforced on the working (orreference) electrode of an electrochemical cell and the current flowingthrough this electrode is measured as a function of time.

The term “pulsed amperometric detection” as used herein is a broad termand is to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation to anelectrochemical flow cell and a controller, which applies the potentialsand monitors current generated by the electrochemical reactions. Thecell can include one or multiple working electrodes at different appliedpotentials. Multiple electrodes can be arranged so that they face thechromatographic flow independently (parallel configuration), orsequentially (series configuration).

The term “linear regression” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to finding a line inwhich a set of data has a minimal measurement from that line. Byproductsof this algorithm include a slope, a y-intercept, and an R-Squared valuethat determine how well the measurement data fits the line.

The term “non-linear regression” as used herein is a broad term and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to fitting a set ofdata to describe the relationship between a response variable and one ormore explanatory variables in a non-linear fashion.

The term “mean” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to the sum of the observationsdivided by the number of observations.

The term “trimmed mean” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to a mean takenafter extreme values in the tails of a variable (e.g., highs and lows)are eliminated or reduced (e.g., “trimmed”). The trimmed meancompensates for sensitivities to extreme values by dropping a certainpercentage of values on the tails. For example, the 50% trimmed mean isthe mean of the values between the upper and lower quartiles. The 90%trimmed mean is the mean of the values after truncating the lowest andhighest 5% of the values. In one example, two highest and two lowestmeasurements are removed from a data set and then the remainingmeasurements are averaged.

The term “non-recursive filter” as used herein is a broad term and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to an equation thatuses moving averages as inputs and outputs.

The terms “recursive filter” and “auto-regressive algorithm” as usedherein are broad terms and are to be given their ordinary and customarymeaning to a person of ordinary skill in the art (and are not to belimited to a special or customized meaning), and furthermore referwithout limitation to an equation in which includes previous averagesare part of the next filtered output. More particularly, the generationof a series of observations whereby the value of each observation ispartly dependent on the values of those that have immediately precededit. One example is a regression structure in which lagged responsevalues assume the role of the independent variables.

The term “signal estimation algorithm factors” as used herein is a broadterm and is to be given its ordinary and customary meaning to a personof ordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation to one ormore algorithms that use historical and/or present signal data streamvalues to estimate unknown signal data stream values. For example,signal estimation algorithm factors can include one or more algorithms,such as linear or non-linear regression. As another example, signalestimation algorithm factors can include one or more sets ofcoefficients that can be applied to one algorithm.

The term “variation” as used herein is a broad term and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a divergence or amount ofchange from a point, line, or set of data. In one embodiment, estimatedanalyte values can have a variation including a range of values outsideof the estimated analyte values that represent a range of possibilitiesbased on known physiological patterns, for example.

The terms “physiological parameters” and “physiological boundaries” asused herein are broad terms and are to be given their ordinary andcustomary meaning to a person of ordinary skill in the art (and are notto be limited to a special or customized meaning), and furthermore referwithout limitation to the parameters obtained from continuous studies ofphysiological data in humans and/or animals. For example, a maximalsustained rate of change of glucose in humans of about 4 to 5 mg/dL/minand a maximum acceleration of the rate of change of about 0.1 to 0.2mg/dL/min² are deemed physiologically feasible limits; values outside ofthese limits would be considered non-physiological. As another example,the rate of change of glucose is lowest at the maxima and minima of thedaily glucose range, which are the areas of greatest risk in patienttreatment, thus a physiologically feasible rate of change can be set atthe maxima and minima based on continuous studies of glucose data. As afurther example, it has been observed that the best solution for theshape of the curve at any point along glucose signal data stream over acertain time period (for example, about 20 to 30 minutes) is a straightline, which can be used to set physiological limits. These terms arebroad enough to include physiological parameters for any analyte.

The term “measured analyte values” as used herein is a broad term and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to an analyte valueor set of analyte values for a time period for which analyte data hasbeen measured by an analyte sensor. The term is broad enough to includedata from the analyte sensor before or after data processing in thesensor and/or receiver (for example, data smoothing, calibration, andthe like).

The term “estimated analyte values” as used herein is a broad term andis to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation to ananalyte value or set of analyte values, which have been algorithmicallyextrapolated from measured analyte values. Typically, estimated analytevalues are estimated for a time period during which no data exists.However, estimated analyte values can also be estimated during a timeperiod for which measured data exists, but is to be replaced byalgorithmically extrapolated (e.g. processed or filtered) data due tonoise or a time lag in the measured data, for example.

The terms “interferants” and “interfering species” as used herein arebroad terms and are to be given their ordinary and customary meaning toa person of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto effects and/or species that interfere with the measurement of ananalyte of interest in a sensor to produce a signal that does notaccurately represent the analyte concentration. In one example of anelectrochemical sensor, interfering species are compounds with anoxidation potential that overlap that of the analyte to be measured,thereby producing a false positive signal.

As employed herein, the following abbreviations apply: Eq and Eqs(equivalents); mEq (milliequivalents); M (molar); mM (millimolar) μM(micromolar); N (Normal); mol (moles); mmol (millimoles); μmol(micromoles); nmol (nanomoles); g (grams); mg (milligrams); μg(micrograms); Kg (kilograms); L (liters); mL (milliliters); dL(deciliters); μL (microliters); cm (centimeters); mm (millimeters); μm(micrometers); nm (nanometers); h and hr (hours); min. (minutes); s andsec. (seconds); ° C. (degrees Centigrade).

Overview

The preferred embodiments relate to the use of a glucose sensor thatmeasures a concentration of glucose or a substance indicative of theconcentration or presence of the glucose. In some embodiments, theglucose sensor is a continuous device, for example a subcutaneous,transdermal, or intravascular device. In some embodiments, the devicecan analyze a plurality of intermittent blood samples. The glucosesensor can use any method of glucose-measurement, including enzymatic,chemical, physical, electrochemical, spectrophotometric, polarimetric,calorimetric, iontophoretic, radiometric, and the like.

The glucose sensor can use any known method, including invasive,minimally invasive, and non-invasive sensing techniques, to provide adata stream indicative of the concentration of glucose in a host. Thedata stream is typically a raw data signal that is used to provide auseful value of glucose to a user, such as a patient or doctor, who maybe using the sensor. It is well known that raw data streams typicallyinclude system noise such as defined herein; however the preferredembodiments address the detection and replacement of “signal artifacts”as defined herein. Accordingly, appropriate signal estimation (e.g.,filtering, data smoothing, augmenting, projecting, and/or other methods)replace such erroneous signals (e.g., signal artifacts) in the raw datastream.

Glucose Sensor

The glucose sensor can be any device capable of measuring theconcentration of glucose. One exemplary embodiment is described below,which utilizes an implantable glucose sensor. However, it should beunderstood that the devices and methods described herein can be appliedto any device capable of detecting a concentration of glucose andproviding an output signal that represents the concentration of glucose.

In one preferred embodiment, the analyte sensor is an implantableglucose sensor, such as described with reference to U.S. Pat. No.6,001,067 and U.S. Publication No. US-2005-0027463-A1. In anotherpreferred embodiment, the analyte sensor is a transcutaneous glucosesensor, such as described with reference to U.S. Publication No.US-2006-0020187-A1. In one alternative embodiment, the continuousglucose sensor comprises a transcutaneous sensor such as described inU.S. Pat. No. 6,565,509 to Say et al., for example.

In another alternative embodiment, the continuous glucose sensorcomprises a subcutaneous sensor such as described with reference to U.S.Pat. No. 6,579,690 to Bonnecaze et al. or U.S. Pat. No. 6,484,046 to Sayet al., for example. In another alternative embodiment, the continuousglucose sensor comprises a refillable subcutaneous sensor such asdescribed with reference to U.S. Pat. No. 6,512,939 to Colvin et al.,for example. In another alternative embodiment, the continuous glucosesensor comprises an intravascular sensor such as described withreference to U.S. Pat. No. 6,477,395 to Schulman et al., for example. Inanother alternative embodiment, the continuous glucose sensor comprisesan intravascular sensor such as described with reference to U.S. Pat.No. 6,424,847 to Mastrototaro et al.

FIG. 1A is an exploded perspective view of one exemplary embodimentcomprising an implantable glucose sensor 10 that utilizes amperometricelectrochemical sensor technology to measure glucose concentration. Inthis exemplary embodiment, a body 12 and head 14 house the electrodes 16and sensor electronics, which are described in more detail below withreference to FIG. 2. Three electrodes 16 are operably connected to thesensor electronics (FIG. 2) and are covered by a sensing membrane 17 anda biointerface membrane 18, which are attached by a clip 19.

In one embodiment, the three electrodes 16, which protrude through thehead 14, include a platinum working electrode, a platinum counterelectrode, and a silver/silver chloride reference electrode. The topends of the electrodes are in contact with an electrolyte phase (notshown), which is a free-flowing fluid phase disposed between the sensingmembrane 17 and the electrodes 16. The sensing membrane 17 includes anenzyme, e.g., glucose oxidase, which covers the electrolyte phase. Thebiointerface membrane 18 covers the sensing membrane 17 and serves, atleast in part, to protect the sensor 10 from external forces that canresult in environmental stress cracking of the sensing membrane 17.

In the illustrated embodiment, the counter electrode is provided tobalance the current generated by the species being measured at theworking electrode. In the case of a glucose oxidase based glucosesensor, the species being measured at the working electrode is H₂O₂.Glucose oxidase catalyzes the conversion of oxygen and glucose tohydrogen peroxide and gluconate according to the following reaction:Glucose+O₂→Gluconate+H₂O₂

The change in H₂O₂ can be monitored to determine glucose concentrationbecause for each glucose molecule metabolized, there is a proportionalchange in the product H₂O₂. Oxidation of H₂O₂ by the working electrodeis balanced by reduction of ambient oxygen, enzyme generated H₂O₂, orother reducible species at the counter electrode. The H₂O₂ produced fromthe glucose oxidase reaction further reacts at the surface of workingelectrode and produces two protons (2H⁺), two electrons (2e⁻), and oneoxygen molecule (O₂).

FIG. 1B is side view of a distal portion of a transcutaneously-insertedsensor 100 in one embodiment, showing working and reference electrodes.In preferred embodiments, the sensor 100 is formed from a workingelectrode 244 and a reference electrode 246 helically wound around theworking electrode 244. An insulator 245 is disposed between the workingand reference electrodes to provide necessary electrical insulationtherebetween. Certain portions of the electrodes are exposed to enableelectrochemical reaction thereon, for example, a window 243 can beformed in the insulator to expose a portion of the working electrode 244for electrochemical reaction.

In preferred embodiments, each electrode is formed from a fine wire witha diameter of from about 0.001 or less to about 0.010 inches or more,for example, and is formed from, e.g., a plated insulator, a platedwire, or bulk electrically conductive material. Although the illustratedelectrode configuration and associated text describe one preferredmethod of forming a transcutaneous sensor, a variety of knowntranscutaneous sensor configurations can be employed with thetranscutaneous analyte sensor system of the preferred embodiments, suchas are described in U.S. Pat. No. 6,695,860 to Ward et al., U.S. Pat.No. 6,565,509 to Say et al., U.S. Pat. No. 6,248,067 to Causey III, etal., and U.S. Pat. No. 6,514,718 to Heller et al.

In preferred embodiments, the working electrode comprises a wire formedfrom a conductive material, such as platinum, platinum-iridium,palladium, graphite, gold, carbon, conductive polymer, alloys, and thelike. Although the electrodes can by formed by a variety ofmanufacturing techniques (bulk metal processing, deposition of metalonto a substrate, and the like), it can be advantageous to form theelectrodes from plated wire (e.g., platinum on steel wire) or bulk metal(e.g., platinum wire). It is believed that electrodes formed from bulkmetal wire provide superior performance (e.g., in contrast to depositedelectrodes), including increased stability of assay, simplifiedmanufacturability, resistance to contamination (e.g., which can beintroduced in deposition processes), and improved surface reaction(e.g., due to purity of material) without peeling or delamination.

The working electrode 244 is configured to measure the concentration ofan analyte. In an enzymatic electrochemical sensor for detectingglucose, for example, the working electrode measures the hydrogenperoxide produced by an enzyme catalyzed reaction of the analyte beingdetected and creates a measurable electronic current. For example, inthe detection of glucose wherein glucose oxidase produces hydrogenperoxide as a byproduct, hydrogen peroxide reacts with the surface ofthe working electrode producing two protons (2H⁺), two electrons (2e⁻)and one molecule of oxygen (O₂), which produces the electronic currentbeing detected.

In preferred embodiments, the working electrode 244 is covered with aninsulating material 45, for example, a non-conductive polymer.Dip-coating, spray-coating, vapor-deposition, or other coating ordeposition techniques can be used to deposit the insulating material onthe working electrode. In one embodiment, the insulating materialcomprises parylene, which can be an advantageous polymer coating for itsstrength, lubricity, and electrical insulation properties. Generally,parylene is produced by vapor deposition and polymerization ofpara-xylylene (or its substituted derivatives). However, any suitableinsulating material can be used, for example, fluorinated polymers,polyethyleneterephthalate, polyurethane, polyimide, other nonconductingpolymers, and the like. Glass or ceramic materials can also be employed.Other materials suitable for use include surface energy modified coatingsystems such as are marketed under the trade names AMC18, AMC148,AMC141, and AMC321 by Advanced Materials Components Express ofBellafonte, Pa. In some alternative embodiments, however, the workingelectrode may not require a coating of insulator.

The reference electrode 246, which can function as a reference electrodealone, or as a dual reference and counter electrode, is formed fromsilver, silver/silver chloride, and the like. Preferably, the referenceelectrode 246 is juxtapositioned and/or twisted with or around theworking electrode 244; however other configurations are also possible.In the illustrated embodiments, the reference electrode 246 is helicallywound around the working electrode 244. The assembly of wires is thenoptionally coated or adhered together with an insulating material,similar to that described above, so as to provide an insulatingattachment.

In embodiments wherein an outer insulator is disposed, a portion of thecoated assembly structure can be stripped or otherwise removed, forexample, by hand, excimer lasing, chemical etching, laser ablation,grit-blasting (e.g., with sodium bicarbonate or other suitable grit),and the like, to expose the electroactive surfaces. Alternatively, aportion of the electrode can be masked prior to depositing the insulatorin order to maintain an exposed electroactive surface area. In oneexemplary embodiment, grit blasting is implemented to expose theelectroactive surfaces, preferably utilizing a grit material that issufficiently hard to ablate the polymer material, while beingsufficiently soft so as to minimize or avoid damage to the underlyingmetal electrode (e.g., a platinum electrode). Although a variety of“grit” materials can be used (e.g., sand, talc, walnut shell, groundplastic, sea salt, and the like), in some preferred embodiments, sodiumbicarbonate is an advantageous grit-material because it is sufficientlyhard to ablate, e.g., a parylene coating without damaging, e.g., anunderlying platinum conductor. One additional advantage of sodiumbicarbonate blasting includes its polishing action on the metal as itstrips the polymer layer, thereby eliminating a cleaning step that mightotherwise be necessary.

In the embodiment illustrated in FIG. 1B, a radial window 243 is formedthrough the insulating material 245 to expose a circumferentialelectroactive surface of the working electrode. Additionally, sections241 of electroactive surface of the reference electrode are exposed. Forexample, the 241 sections of electroactive surface can be masked duringdeposition of an outer insulating layer or etched after deposition of anouter insulating layer.

In some applications, cellular attack or migration of cells to thesensor can cause reduced sensitivity and/or function of the device,particularly after the first day of implantation. However, when theexposed electroactive surface is distributed circumferentially about thesensor (e.g., as in a radial window), the available surface area forreaction can be sufficiently distributed so as to minimize the effect oflocal cellular invasion of the sensor on the sensor signal.Alternatively, a tangential exposed electroactive window can be formed,for example, by stripping only one side of the coated assemblystructure. In other alternative embodiments, the window can be providedat the tip of the coated assembly structure such that the electroactivesurfaces are exposed at the tip of the sensor. Other methods andconfigurations for exposing electroactive surfaces can also be employed.

In some embodiments, the working electrode has a diameter of from about0.001 inches or less to about 0.010 inches or more, preferably fromabout 0.002 inches to about 0.008 inches, and more preferably from about0.004 inches to about 0.005 inches. The length of the window can be fromabout 0.1 mm (about 0.004 inches) or less to about 2 mm (about 0.078inches) or more, and preferably from about 0.5 mm (about 0.02 inches) toabout 0.75 mm (0.03 inches). In such embodiments, the exposed surfacearea of the working electrode is preferably from about 0.000013 in²(0.0000839 cm²) or less to about 0.0025 in² (0.016129 cm²) or more(assuming a diameter of from about 0.001 inches to about 0.010 inchesand a length of from about 0.004 inches to about 0.078 inches). Thepreferred exposed surface area of the working electrode is selected toproduce an analyte signal with a current in the picoAmp range, such asis described in more detail elsewhere herein. However, a current in thepicoAmp range can be dependent upon a variety of factors, for examplethe electronic circuitry design (e.g., sample rate, current draw, A/Dconverter bit resolution, etc.), the membrane system (e.g., permeabilityof the analyte through the membrane system), and the exposed surfacearea of the working electrode. Accordingly, the exposed electroactiveworking electrode surface area can be selected to have a value greaterthan or less than the above-described ranges taking into considerationalterations in the membrane system and/or electronic circuitry. Inpreferred embodiments of a glucose sensor, it can be advantageous tominimize the surface area of the working electrode while maximizing thediffusivity of glucose in order to optimize the signal-to-noise ratiowhile maintaining sensor performance in both high and low glucoseconcentration ranges.

In some alternative embodiments, the exposed surface area of the working(and/or other) electrode can be increased by altering the cross-sectionof the electrode itself. For example, in some embodiments thecross-section of the working electrode can be defined by a cross, star,cloverleaf, ribbed, dimpled, ridged, irregular, or other non-circularconfiguration; thus, for any predetermined length of electrode, aspecific increased surface area can be achieved (as compared to the areaachieved by a circular cross-section). Increasing the surface area ofthe working electrode can be advantageous in providing an increasedsignal responsive to the analyte concentration, which in turn can behelpful in improving the signal-to-noise ratio, for example.

In some alternative embodiments, additional electrodes can be includedwithin the assembly, for example, a three-electrode system (working,reference, and counter electrodes) and/or an additional workingelectrode (e.g., an electrode which can be used to generate oxygen,which is configured as a baseline subtracting electrode, or which isconfigured for measuring additional analytes). U.S. Publication No.US-2005-0161346-A1 and U.S. Publication No. US-2005-0143635-A1 describesome systems and methods for implementing and using additional working,counter, and/or reference electrodes. In one implementation wherein thesensor comprises two working electrodes, the two working electrodes arejuxtapositioned (e.g., extend parallel to each other), around which thereference electrode is disposed (e.g., helically wound). In someembodiments wherein two or more working electrodes are provided, theworking electrodes can be formed in a double-, triple-, quad-, etc.helix configuration along the length of the sensor (for example,surrounding a reference electrode, insulated rod, or other supportstructure). The resulting electrode system can be configured with anappropriate membrane system, wherein the first working electrode isconfigured to measure a first signal comprising glucose and baseline andthe additional working electrode is configured to measure a baselinesignal consisting of baseline only (e.g., configured to be substantiallysimilar to the first working electrode without an enzyme disposedthereon). In this way, the baseline signal can be subtracted from thefirst signal to produce a glucose-only signal that is substantially notsubject to fluctuations in the baseline and/or interfering species onthe signal.

Although the preferred embodiments illustrate one electrodeconfiguration including one bulk metal wire helically wound aroundanother bulk metal wire, other electrode configurations are alsocontemplated. In an alternative embodiment, the working electrodecomprises a tube with a reference electrode disposed or coiled inside,including an insulator therebetween. Alternatively, the referenceelectrode comprises a tube with a working electrode disposed or coiledinside, including an insulator therebetween. In another alternativeembodiment, a polymer (e.g., insulating) rod is provided, wherein theelectrodes are deposited (e.g., electro-plated) thereon. In yet anotheralternative embodiment, a metallic (e.g., steel) rod is provided, coatedwith an insulating material, onto which the working and referenceelectrodes are deposited. In yet another alternative embodiment, one ormore working electrodes are helically wound around a referenceelectrode.

Preferably, the electrodes and membrane systems of the preferredembodiments are coaxially formed, namely, the electrodes and/or membranesystem all share the same central axis. While not wishing to be bound bytheory, it is believed that a coaxial design of the sensor enables asymmetrical design without a preferred bend radius. Namely, in contrastto prior art sensors comprising a substantially planar configurationthat can suffer from regular bending about the plane of the sensor, thecoaxial design of the preferred embodiments do not have a preferred bendradius and therefore are not subject to regular bending about aparticular plane (which can cause fatigue failures and the like).However, non-coaxial sensors can be implemented with the sensor systemof the preferred embodiments.

In addition to the above-described advantages, the coaxial sensor designof the preferred embodiments enables the diameter of the connecting endof the sensor (proximal portion) to be substantially the same as that ofthe sensing end (distal portion) such that the needle is able to insertthe sensor into the host and subsequently slide back over the sensor andrelease the sensor from the needle, without slots or other complexmulti-component designs.

In one such alternative embodiment, the two wires of the sensor are heldapart and configured for insertion into the host in proximal butseparate locations. The separation of the working and referenceelectrodes in such an embodiment can provide additional electrochemicalstability with simplified manufacture and electrical connectivity. It isappreciated by one skilled in the art that a variety of electrodeconfigurations can be implemented with the preferred embodiments.

Preferably, a membrane system is deposited over the electroactivesurfaces of the sensor 100 and includes a plurality of domains orlayers. The membrane system may be deposited on the exposedelectroactive surfaces using known thin film techniques (for example,spraying, electro-depositing, dipping, and the like). In one exemplaryembodiment, each domain is deposited by dipping the sensor into asolution and drawing out the sensor at a speed that provides theappropriate domain thickness. In general, the membrane system may bedisposed over (deposited on) the electroactive surfaces using methodsappreciated by one skilled in the art.

In one exemplary embodiment, the sensor is an enzyme-basedelectrochemical sensor, wherein the glucose-measuring working electrodemeasures the hydrogen peroxide produced by the enzyme catalyzed reactionof glucose being detected and creates a measurable electronic current(for example, detection of glucose utilizing glucose oxidase producesH₂O₂ peroxide as a by product, H₂O₂ reacts with the surface of theworking electrode producing two protons (2H⁺), two electrons (2e⁻) andone molecule of oxygen (O₂) which produces the electronic current beingdetected), such as described in more detail above and as is appreciatedby one skilled in the art. Typically, the working and referenceelectrodes operatively connect with sensor electronics, such asdescribed in more detail elsewhere herein. Additional aspects of theabove-described transcutaneously inserted sensor can be found inco-pending U.S. Publication No. US-2006-0020187-A1.

In some embodiments (e.g., sensors such as illustrated in FIGS. 1A and1B), a potentiostat is employed to monitor the electrochemical reactionat the electrochemical cell. The potentiostat applies a constantpotential to the working and reference electrodes to determine a currentvalue. The current that is produced at the working electrode (and flowsthrough the circuitry to the counter electrode) is proportional to theamount of H₂O₂ that diffuses to the working electrode. Accordingly, araw signal can be produced that is representative of the concentrationof glucose in the user's body, and therefore can be utilized to estimatea meaningful glucose value, such as described herein.

One problem with raw data stream output of enzymatic glucose sensorssuch as described above is caused by transient non-glucose reactionrate-limiting phenomenon. For example, if oxygen is deficient, relativeto the amount of glucose, then the enzymatic reaction will be limited byoxygen rather than glucose. Consequently, the output signal will beindicative of the oxygen concentration rather than the glucoseconcentration, producing erroneous signals. Other non-glucose reactionrate-limiting phenomenon could include interfering species, temperatureand/or pH changes, or even unknown sources of mechanical, electricaland/or biochemical noise, for example. Accordingly, reduction of signalnoise, and particularly replacement of transient non-glucose relatedsignal artifacts in the data stream that have a higher amplitude thansystem noise, can be performed in the sensor and/or in the receiver,such as described in more detail below in the sections entitled “SignalArtifacts Detection” and “Signal Artifacts Replacement,” for example.

FIG. 2 is a block diagram that illustrates one possible configuration ofthe sensor electronics in one embodiment. In this embodiment, apotentiostat 20 is shown, which is operatively connected to an electrodesystem (FIG. 1A or 1B) and provides a voltage to the electrodes, whichbiases the sensor to enable measurement of a current value indicative ofthe analyte concentration in the host (also referred to as the analogportion). In some embodiments, the potentiostat includes a resistor (notshown) that translates the current into voltage. In some alternativeembodiments, a current to frequency converter is provided that isconfigured to continuously integrate the measured current, for example,using a charge counting device. In the illustrated embodiment, an A/Dconverter 21 digitizes the analog signal into “counts” for processing.Accordingly, the resulting raw data stream in counts is directly relatedto the current measured by the potentiostat 20.

A processor module 22 is the central control unit that controls theprocessing of the sensor electronics. In some embodiments, the processormodule includes a microprocessor, however a computer system other than amicroprocessor can be used to process data as described herein, forexample an ASIC can be used for some or all of the sensor's centralprocessing. The processor typically provides semi-permanent storage ofdata, for example, storing data such as sensor identifier (ID) andprogramming to process data streams (for example, programming for datasmoothing and/or replacement of signal artifacts such as is described inmore detail elsewhere herein). The processor additionally can be usedfor the system's cache memory, for example for temporarily storingrecent sensor data. In some embodiments, the processor module comprisesmemory storage components such as ROM, RAM, dynamic-RAM, static-RAM,non-static RAM, EEPROM, rewritable ROMs, flash memory, and the like. Inone exemplary embodiment, EEPROM 23 provides semi-permanent storage ofdata, for example, storing data such as sensor identifier (ID) andprogramming to process data streams (e.g., programming for signalartifacts detection and/or replacement such as described elsewhereherein). In one exemplary embodiment, SRAM 24 can be used for thesystem's cache memory, for example for temporarily storing recent sensordata.

In some embodiments, the processor module comprises a digital filter,for example, an IIR or FIR filter, configured to smooth the raw datastream from the A/D converter. Generally, digital filters are programmedto filter data sampled at a predetermined time interval (also referredto as a sample rate). In some embodiments, wherein the potentiostat isconfigured to measure the analyte at discrete time intervals, these timeintervals determine the sample rate of the digital filter. In somealternative embodiments, wherein the potentiostat is configured tocontinuously measure the analyte, for example, using acurrent-to-frequency converter, the processor module can be programmedto request a digital value from the A/D converter at a predeterminedtime interval, also referred to as the acquisition time. In thesealternative embodiments, the values obtained by the processor areadvantageously averaged over the acquisition time due the continuity ofthe current measurement. Accordingly, the acquisition time determinesthe sample rate of the digital filter. In preferred embodiments, theprocessor module is configured with a programmable acquisition time,namely, the predetermined time interval for requesting the digital valuefrom the A/D converter is programmable by a user within the digitalcircuitry of the processor module. An acquisition time of from about 2seconds to about 512 seconds is preferred; however any acquisition timecan be programmed into the processor module. A programmable acquisitiontime is advantageous in optimizing noise filtration, time lag, andprocessing/battery power.

Preferably, the processor module is configured to build the data packetfor transmission to an outside source, for example, an RF transmissionto a receiver as described in more detail below. Generally, the datapacket comprises a plurality of bits that can include a sensor ID code,raw data, filtered data, and/or error detection or correction. Theprocessor module can be configured to transmit any combination of rawand/or filtered data.

A battery 25 is operatively connected to the processor 22 and providesthe necessary power for the sensor (e.g., 10 or 100). In one embodiment,the battery is a Lithium Manganese Dioxide battery, however anyappropriately sized and powered battery can be used (e.g., AAA,Nickel-cadmium, Zinc-carbon, Alkaline, Lithium, Nickel-metal hydride,Lithium-ion, Zinc-air, Zinc-mercury oxide, Silver-zinc, orhermetically-sealed). In some embodiments the battery is rechargeable.In some embodiments, a plurality of batteries can be used to power thesystem. In yet other embodiments, the receiver can be transcutaneouslypowered via an inductive coupling, for example. A Quartz Crystal 26 isoperatively connected to the processor 22 and maintains system time forthe computer system as a whole.

An RF module, (e.g., an RF Transceiver) 27 is operably connected to theprocessor 22 and transmits the sensor data from the sensor (e.g., 10 or100) to a receiver (see FIGS. 3 and 4). Although an RF transceiver isshown here, some other embodiments can include a wired rather thanwireless connection to the receiver. A second quartz crystal 28 providesthe system time for synchronizing the data transmissions from the RFtransceiver. It is noted that the transceiver 27 can be substituted witha transmitter in other embodiments. In some alternative embodiments,however, other mechanisms, such as optical, infrared radiation (IR),ultrasonic, and the like, can be used to transmit and/or receive data.

In some embodiments, a Signal Artifacts Detector 29 is provided thatincludes one or more of the following: an oxygen detector 29 a, a pHdetector 29 b, a temperature detector 29 c, and a pressure/stressdetector 29 d, which is described in more detail with reference tosignal artifacts detection. It is noted that in some embodiments thesignal artifacts detector 29 is a separate entity (e.g., temperaturedetector) operatively connected to the processor, while in otherembodiments, the signal artifacts detector is a part of the processorand utilizes readings from the electrodes, for example, to detectischemia and other signal artifacts. Although the above description isfocused on an embodiment of the Signal Artifacts Detector within thesensor, some embodiments provide for systems and methods for detectingsignal artifacts in the sensor and/or receiver electronics (e.g.,processor module) as described in more detail elsewhere herein.

Receiver

FIGS. 3A to 3D are schematic views of a receiver 30 includingrepresentations of estimated glucose values on its user interface infirst, second, third, and fourth embodiments, respectively. The receiver30 comprises systems to receive, process, and display sensor data fromthe glucose sensor (e.g., 10 or 100), such as described herein.Particularly, the receiver 30 can be a pager-sized device, for example,and comprise a user interface that has a plurality of buttons 32 and aliquid crystal display (LCD) screen 34, and which can optionally includea backlight. In some embodiments, the user interface can also include akeyboard, a speaker, and a vibrator, as described below with referenceto FIG. 4A.

FIG. 3A illustrates a first embodiment wherein the receiver 30 shows anumeric representation of the estimated glucose value on its userinterface, which is described in more detail elsewhere herein.

FIG. 3B illustrates a second embodiment wherein the receiver 30 shows anestimated glucose value and approximately one hour of historical trenddata on its user interface, which is described in more detail elsewhereherein.

FIG. 3C illustrates a third embodiment wherein the receiver 30 shows anestimated glucose value and approximately three hours of historicaltrend data on its user interface, which is described in more detailelsewhere herein.

FIG. 3D illustrates a fourth embodiment wherein the receiver 30 shows anestimated glucose value and approximately nine hours of historical trenddata on its user interface, which is described in more detail elsewhereherein.

In some embodiments, a user can toggle through some or all of thescreens shown in FIGS. 3A to 3D using a toggle button on the receiver.In some embodiments, the user will be able to interactively select thetype of output displayed on their user interface. In other embodiments,the sensor output can have alternative configurations.

FIG. 4A is a block diagram that illustrates one possible configurationof the receiver's 30 electronics. It is noted that the receiver 30 cancomprise a configuration such as described with reference to FIGS. 3A to3D, above. Alternatively, the receiver 30 can comprise otherconfigurations, including a desktop computer, laptop computer, apersonal digital assistant (PDA), a server (local or remote to thereceiver), and the like. In some embodiments, the receiver 30 can beadapted to connect (via wired or wireless connection) to a desktopcomputer, laptop computer, PDA, server (local or remote to thereceiver), and the like, in order to download data from the receiver 30.In some alternative embodiments, the receiver 30 and/or receiverelectronics can be housed within or directly connected to the sensor(e.g., 10 or 100) in a manner that allows sensor and receiverelectronics to work directly together and/or share data processingresources. Accordingly, the receiver's electronics can be generallyreferred to as a “computer system.”

A quartz crystal 40 is operatively connected to an RF transceiver 41that together function to receive and synchronize data streams (e.g.,raw data streams transmitted from the RF transceiver). Once received, aprocessor 42 processes the signals, such as described below.

The processor 42, also referred to as the processor module, is thecentral control unit that performs the processing, such as storing data,analyzing data streams, calibrating analyte sensor data, estimatinganalyte values, comparing estimated analyte values with timecorresponding measured analyte values, analyzing a variation ofestimated analyte values, downloading data, and controlling the userinterface by providing analyte values, prompts, messages, warnings,alarms, and the like. The processor includes hardware and software thatperforms the processing described herein, for example flash memoryprovides permanent or semi-permanent storage of data, storing data suchas sensor ID, receiver ID, and programming to process data streams (forexample, programming for performing estimation and other algorithmsdescribed elsewhere herein) and random access memory (RAM) stores thesystem's cache memory and is helpful in data processing.

In one exemplary embodiment, the processor is a microprocessor thatprovides the processing, such as calibration algorithms stored within anEEPROM 43. The EEPROM 43 is operatively connected to the processor 42and provides semi-permanent storage of data, storing data such asreceiver ID and programming to process data streams (e.g., programmingfor performing calibration and other algorithms described elsewhereherein). In this exemplary embodiment, an SRAM 44 is used for thesystem's cache memory and is helpful in data processing.

A battery 45 is operatively connected to the processor 42 and providespower for the receiver. In one embodiment, the battery is a standard AAAalkaline battery, however any appropriately sized and powered batterycan be used. In some embodiments, a plurality of batteries can be usedto power the system. A quartz crystal 46 is operatively connected to theprocessor 42 and maintains system time for the computer system as awhole.

A user interface 47 comprises a keyboard 2, speaker 3, vibrator 4,backlight 5, liquid crystal display (LCD 6), and one or more buttons 7.The components that comprise the user interface 47 provide controls tointeract with the user. The keyboard 2 can allow, for example, input ofuser information about himself/herself, such as mealtime, exercise,insulin administration, and reference glucose values. The speaker 3 canprovide, for example, audible signals or alerts for conditions such aspresent and/or predicted hyper- and hypoglycemic conditions. Thevibrator 4 can provide, for example, tactile signals or alerts forreasons such as described with reference to the speaker, above. Thebacklight 5 can be provided, for example, to aid the user in reading theLCD in low light conditions. The LCD 6 can be provided, for example, toprovide the user with visual data output such as is illustrated in FIGS.3A to 3D. The buttons 7 can provide for toggle, menu selection, optionselection, mode selection, and reset, for example.

In some embodiments, prompts or messages can be displayed on the userinterface to convey information to the user, such as reference outliervalues, requests for reference analyte values, therapy recommendations,deviation of the measured analyte values from the estimated analytevalues, and the like. Additionally, prompts can be displayed to guidethe user through calibration or trouble-shooting of the calibration.

Input and Output

In general, the above-described estimative algorithms, includingestimation of measured analyte values and variation analysis of theestimated analyte values are useful when provided to a patient, doctor,family member, and the like. Even more, the estimative algorithms areuseful when they are able to provide information helpful in modifying apatient's behavior so that they experience less clinically riskysituations and higher quality of life than may otherwise be possible.Therefore, the above-described data analysis can be output in a varietyof forms useful in caring for the health of a patient.

Output can be provided via a user interface, including but not limitedto, visually on a screen, audibly through a speaker, or tactilelythrough a vibrator. Additionally, output can be provided via wired orwireless connection to an external device, including but not limited to,computer, laptop, server, personal digital assistant, modem connection,insulin delivery mechanism, medical device, or other device that can beuseful in interfacing with the receiver.

Output can be continuously provided, or certain output can beselectively provided based on events, analyte concentrations and thelike. For example, an estimated analyte path can be continuouslyprovided to a patient on an LCD screen, while audible alerts can beprovided only during a time of existing or approaching clinical risk toa patient. As another example, estimation can be provided based on eventtriggers (for example, when an analyte concentration is nearing orentering a clinically risky zone). As yet another example, analyzeddeviation of estimated analyte values can be provided when apredetermined level of variation (for example, due to known error orclinical risk) is known.

In some embodiments, alarms prompt or alert a patient when a measured orprojected analyte value or rate of change simply passes a predeterminedthreshold. In some embodiments, the clinical risk alarms combineintelligent and dynamic estimative algorithms to provide greateraccuracy, more timeliness in pending danger, avoidance of false alarms,and less annoyance for the patient. For example, clinical risk alarms ofthese embodiments include dynamic and intelligent estimative algorithmsbased on analyte value, rate of change, acceleration, clinical risk,statistical probabilities, known physiological constraints, and/orindividual physiological patterns, thereby providing more appropriate,clinically safe, and patient-friendly alarms.

In some embodiments, clinical risk alarms can be activated for apredetermined time period to allow for the user to attend to his/hercondition. Additionally, the clinical risk alarms can be de-activatedwhen leaving a clinical risk zone so as not to annoy the patient byrepeated clinical risk alarms, when the patient's condition isimproving.

In some embodiments, the dynamic and intelligent estimation determines apossibility of the patient avoiding clinical risk, based on the analyteconcentration, the rate of change, and other aspects of the dynamic andintelligent estimative algorithms of the preferred embodiments. If thereis minimal or no possibility of avoiding the clinical risk, a clinicalrisk alarm will be triggered. However, if there is a possibility ofavoiding the clinical risk, the system can wait a predetermined amountof time and re-analyze the possibility of avoiding the clinical risk. Insome embodiments, when there is a possibility of avoiding the clinicalrisk, the system will further provide targets, therapy recommendations,or other information that can aid the patient in proactively avoidingthe clinical risk.

In some embodiments, a variety of different display methods are used,such as described in the preferred embodiments, which can be toggledthrough or selectively displayed to the user based on conditions or byselecting a button, for example. As one example, a simple screen can benormally shown that provides an overview of analyte data, for examplepresent analyte value and directional trend. More complex screens canthen be selected when a user desires more detailed information, forexample, historical analyte data, alarms, clinical risk zones, and thelike.

FIG. 4B is an illustration of the receiver in one embodiment showing ananalyte trend graph, including measured analyte values, estimatedanalyte values, and a clinical risk zone. The receiver 30 includes anLCD screen 34, buttons 7, and a speaker 3 and/or microphone. The screen34 displays a trend graph in the form of a line representing thehistorical trend of a patient's analyte concentration. Although axes mayor may not be shown on the screen 34, it is understood that atheoretical x-axis represents time and a theoretical y-axis representsanalyte concentration.

In some embodiments such as shown in FIG. 4B, the screen showsthresholds, including a high threshold 200 and a low threshold 202,which represent boundaries between clinically safe and clinically riskyconditions for the patients. In one exemplary embodiment, a normalglucose threshold for a glucose sensor is set between about 100 and 160mg/dL, and the clinical risk zones 204 are illustrated outside of thesethresholds. In alternative embodiments, the normal glucose threshold isbetween about 80 and about 200 mg/dL, between about 55 and about 220mg/dL, or other threshold that can be set by the manufacturer,physician, patient, computer program, and the like. Although a fewexamples of glucose thresholds are given for a glucose sensor, thesetting of any analyte threshold is not limited by the preferredembodiments.

In some embodiments, the screen 34 shows clinical risk zones 204, alsoreferred to as danger zones, through shading, gradients, or othergraphical illustrations that indicate areas of increasing clinical risk.Clinical risk zones 204 can be set by a manufacturer, customized by adoctor, and/or set by a user via buttons 7, for example. In someembodiments, the danger zone 204 can be continuously shown on the screen34, or the danger zone can appear when the measured and/or estimatedanalyte values fall into the danger zone 204. Additional information canbe displayed on the screen, such as an estimated time to clinical risk.In some embodiments, the danger zone can be divided into levels ofdanger (for example, low, medium, and high) and/or can be color-coded(for example, yellow, orange, and red) or otherwise illustrated toindicate the level of danger to the patient. Additionally, the screen orportion of the screen can dynamically change colors or illustrationsthat represent a nearness to the clinical risk and/or a severity ofclinical risk.

In some embodiments, such as shown in FIG. 4B, the screen 34 displays atrend graph of measured analyte data 206. Measured analyte data can besmoothed and calibrated such as described in more detail elsewhereherein. Measured analyte data can be displayed for a certain time period(for example, previous 1 hour, 3 hours, 9 hours, etc.) In someembodiments, the user can toggle through screens using buttons 7 to viewthe measured analyte data for different time periods, using differentformats, or to view certain analyte values (for example, highs andlows).

In some embodiments such as shown in FIG. 4B, the screen 34 displaysestimated analyte data 208 using dots. In this illustration, the size ofthe dots can represent the confidence of the estimation, a variation ofestimated values, and the like. For example, as the time gets fartheraway from the present (t=0) the confidence level in the accuracy of theestimation can decline as is appreciated by one skilled in the art. Insome alternative embodiments, dashed lines, symbols, icons, and the likecan be used to represent the estimated analyte values. In somealternative embodiments, shaded regions, colors, patterns, and the likecan also be used to represent the estimated analyte values, a confidencein those values, and/or a variation of those values, such as describedin more detail in preferred embodiments.

Axes, including time and analyte concentration values, can be providedon the screen, however are not required. While not wishing to be boundby theory, it is believed that trend information, thresholds, and dangerzones provide sufficient information to represent analyte concentrationand clinically educate the user. In some embodiments, time can berepresented by symbols, such as a sun and moon to represent day andnight. In some embodiments, the present or most recent measured analyteconcentration, from the continuous sensor and/or from the referenceanalyte monitor can be continually, intermittently, or selectivelydisplayed on the screen.

The estimated analyte values 208 of FIG. 4B include a portion, whichextends into the danger zone 204. By providing data in a format thatemphasizes the possibility of clinical risk to the patient, appropriateaction can be taken by the user (for example, patient, or caretaker) andclinical risk can be preempted.

FIG. 4C is an illustration of the receiver in another embodiment showinga representation of analyte concentration and directional trend using agradient bar. In this embodiment, the screen illustrates the measuredanalyte values and estimated analyte values in a simple but effectivemanner that communicates valuable analyte information to the user.

In this embodiment, a gradient bar 210 is provided that includesthresholds 212 set at high and lows such as described in more detailwith reference to FIG. 4B, above. Additionally, colors, shading, orother graphical illustration can be present to represent danger zones214 on the gradient bar 210 such as described in more detail withreference to FIG. 4B, above.

The measured analyte value is represented on the gradient bar 210 by amarker 216, such as a darkened or colored bar. By representing themeasured analyte value with a bar 216, a low-resolution analyte value ispresented to the user (for example, within a range of values). Forexample, each segment on the gradient bar 210 can represent about 10mg/dL of glucose concentration. As another example, each segment candynamically represent the range of values that fall within the “A” and“B” regions of the Clarke Error Grid. While not wishing to be bound bytheory, it is believed that inaccuracies known both in reference analytemonitors and/or continuous analyte sensors are likely due to knownvariables such as described in more detail elsewhere herein, and can bede-emphasized such that a user focuses on proactive care of thecondition, rather than inconsequential discrepancies within and betweenreference analyte monitors and continuous analyte sensors.

Additionally, the representative gradient bar communicates thedirectional trend of the analyte concentration to the user in a simpleand effective manner, namely by a directional arrow 218. For example, inconventional diabetic blood glucose monitoring, a person with diabetesobtains a blood sample and measures the glucose concentration using atest strip, and the like. Unfortunately, this information does not tellthe person with diabetes whether the blood glucose concentration isrising or falling. Rising or falling directional trend information canbe particularly important in a situation such as illustrated in FIG. 4C,wherein if the user does not know that the glucose concentration isrising, he/she may assume that the glucose concentration is falling andnot attend to his/her condition. However, because rising directionaltrend information 218 is provided, the person with diabetes can preemptthe clinical risk by attending to his/her condition (for example,administer insulin). Estimated analyte data can be incorporated into thedirectional trend information by characteristics of the arrow, forexample, size, color, flash speed, and the like.

In some embodiments, the gradient bar can be a vertical instead ofhorizontal bar. In some embodiments, a gradient fill can be used torepresent analyte concentration, variation, or clinical risk, forexample. In some embodiments, the bar graph includes color, for examplethe center can be green in the safe zone that graduates to red in thedanger zones; this can be in addition to or in place of the dividedsegments. In some embodiments, the segments of the bar graph are clearlydivided by lines; however color, gradation, and the like can be used torepresent areas of the bar graph. In some embodiments, the directionalarrow can be represented by a cascading level of arrows to a representslow or rapid rate of change. In some embodiments, the directional arrowcan be flashing to represent movement or pending danger.

The screen 34 of FIG. 4C can further comprise a numerical representationof analyte concentration, date, time, or other information to becommunicated to the patient. However, a user can advantageouslyextrapolate information helpful for his/her condition using the simpleand effective representation of this embodiment shown in FIG. 4C,without reading a numeric representation of his/her analyteconcentration.

In some alternative embodiments, a trend graph or gradient bar, a dial,pie chart, or other visual representation can provide analyte data usingshading, colors, patterns, icons, animation, and the like.

FIG. 4D is an illustration of a receiver 30 in another embodiment,including a screen 34 that shows a numerical representation of the mostrecent measured analyte value 252. This numerical value 252 ispreferably a calibrated analyte value, such as described in more detailwith reference to FIGS. 5 and 6. Additionally, this embodimentpreferably provides an arrow 254 on the screen 34, which represents therate of change of the host's analyte concentration. A bold “up” arrow isshown on the drawing, which preferably represents a relatively quicklyincreasing rate of change. The arrows shown with dotted lines illustrateexamples of other directional arrows (for example, rotated by 45degrees), which can be useful on the screen to represent various otherpositive and negative rates of change. Although the directional arrowsshown have a relative low resolution (45 degrees of accuracy), otherarrows can be rotated with a high resolution of accuracy (for exampleone degree of accuracy) to more accurately represent the rate of changeof the host's analyte concentration. In some alternative embodiments,the screen provides an indication of the acceleration of the host'sanalyte concentration.

A second numerical value 256 is shown, which is representative of avariation of the measured analyte value 252. The second numerical valueis preferably determined from a variation analysis based on statistical,clinical, or physiological parameters, such as described in more detailelsewhere herein. In one embodiment, the second numerical value 256 isdetermined based on clinical risk (for example, weighted for thegreatest possible clinical risk to a patient). In another embodiment,the second numerical representation 256 is an estimated analyte valueextrapolated to compensate for a time lag, such as described in moredetail elsewhere herein. In some alternative embodiments, the receiverdisplays a range of numerical analyte values that best represents thehost's estimated analyte value (for example, +/−10%). In someembodiments, the range is weighted based on clinical risk to thepatient. In some embodiments, the range is representative of aconfidence in the estimated analyte value and/or a variation of thosevalues. In some embodiments, the range is adjustable.

Referring again to FIG. 4A, communication ports, including a PCcommunication (com) port 48 and a reference glucose monitor com port 49can be provided to enable communication with systems that are separatefrom, or integral with, the receiver 30. The PC com port 48, forexample, a serial communications port, allows for communicating withanother computer system (e.g., PC, PDA, server, and the like). In oneexemplary embodiment, the receiver 30 is able to download historicaldata to a physician's PC for retrospective analysis by the physician.The reference glucose monitor com port 49 allows for communicating witha reference glucose monitor (not shown) so that reference glucose valuescan be downloaded into the receiver 30, for example, automatically. Inone embodiment, the reference glucose monitor is integral with thereceiver 30, and the reference glucose com port 49 allows internalcommunication between the two integral systems. In another embodiment,the reference glucose monitor com port 49 allows a wireless or wiredconnection to reference glucose monitor such as a self-monitoring bloodglucose monitor (e.g., for measuring finger stick blood samples).

Calibration

Reference is now made to FIG. 5, which is a flow chart 50 thatillustrates the process of initial calibration and data output of theglucose sensor (e.g., 10 or 100) in one embodiment.

Calibration of the glucose sensor comprises data processing thatconverts a sensor data stream into an estimated glucose measurement thatis meaningful to a user. Accordingly, a reference glucose value can beused to calibrate the data stream from the glucose sensor. In oneembodiment, the analyte sensor is a continuous glucose sensor and one ormore reference glucose values are used to calibrate the data stream fromthe sensor. The calibration can be performed on a real-time basis and/orretrospectively recalibrated. However in alternative embodiments, othercalibration techniques can be utilized, for example using anotherconstant analyte (for example, folic acid, ascorbate, urate, and thelike) as a baseline, factory calibration, periodic clinical calibration,oxygen calibration (for example, using a plurality of sensor heads), andthe like can be used.

At block 51, a sensor data receiving module, also referred to as thesensor data module, or processor module, receives sensor data (e.g., adata stream), including one or more time-spaced sensor data pointshereinafter referred to as “data stream,” “sensor data,” “sensor analytedata”, “glucose signal,” from a sensor via the receiver, which can be inwired or wireless communication with the sensor. The sensor data can beraw or smoothed (filtered), or include both raw and smoothed data. Insome embodiments, raw sensor data may include an integrated digital datavalue, e.g., a value averaged over a time period such as by a chargecapacitor. Smoothed sensor data point(s) can be filtered in certainembodiments using a filter, for example, a finite impulse response (FIR)or infinite impulse response (IIR) filter. Some or all of the sensordata point(s) can be replaced by estimated signal values to addresssignal noise such as described in more detail elsewhere herein. It isnoted that during the initialization of the sensor, prior to initialcalibration, the receiver 30 (e.g., computer system) receives and storesthe sensor data, however it may not display any data to the user untilinitial calibration and eventually stabilization of the sensor has beendetermined.

At block 52, a reference data receiving module, also referred to as thereference input module, or the processor module, receives reference datafrom a reference glucose monitor, including one or more reference datapoints. In one embodiment, the reference glucose points can compriseresults from a self-monitored blood glucose test (e.g., from a fingerstick test). In one such embodiment, the user can administer aself-monitored blood glucose test to obtain a glucose value (e.g.,point) using any known glucose sensor, and enter the numeric glucosevalue into the computer system. In another such embodiment, aself-monitored blood glucose test comprises a wired or wirelessconnection to the receiver 30 (e.g. computer system) so that the usersimply initiates a connection between the two devices, and the referenceglucose data is passed or downloaded between the self-monitored bloodglucose test and the receiver 30. In yet another such embodiment, theself-monitored glucose test is integral with the receiver 30 so that theuser simply provides a blood sample to the receiver 30, and the receiver30 runs the glucose test to determine a reference glucose value.

In some embodiments, the calibration process 50 monitors the continuousanalyte sensor data stream to determine a preferred time for capturingreference analyte concentration values for calibration of the continuoussensor data stream. In an example wherein the analyte sensor is acontinuous glucose sensor, when data (for example, observed from thedata stream) changes too rapidly, the reference glucose value may not besufficiently reliable for calibration due to unstable glucose changes inthe host. In contrast, when sensor glucose data are relatively stable(for example, relatively low rate of change), a reference glucose valuecan be taken for a reliable calibration. In one embodiment, thecalibration process 38 can prompt the user via the user interface to“calibrate now” when the analyte sensor is considered stable.

In some embodiments, the calibration process 50 can prompt the user viathe user interface 47 to obtain a reference analyte value forcalibration at intervals, for example when analyte concentrations are athigh and/or low values. In some additional embodiments, the userinterface 47 can prompt the user to obtain a reference analyte value forcalibration based upon certain events, such as meals, exercise, largeexcursions in analyte levels, faulty or interrupted data readings, andthe like. In some embodiments, the estimative algorithms can provideinformation useful in determining when to request a reference analytevalue. For example, when estimated analyte values indicate approachingclinical risk, the user interface 47 can prompt the user to obtain areference analyte value.

Certain acceptability parameters can be set for reference valuesreceived from the user. For example, in one embodiment, the receiver mayonly accept reference glucose values between about 40 and about 400mg/dL.

In some embodiments, the calibration process 50 performs outlierdetection on the reference data and time corresponding sensor data.Outlier detection compares a reference analyte value with a timecorresponding measured analyte value to ensure a predeterminedstatistically, physiologically, or clinically acceptable correlationbetween the corresponding data exists. In an example wherein the analytesensor is a glucose sensor, the reference glucose data is matched withsubstantially time corresponding calibrated sensor data and the matcheddata are plotted on a Clarke Error Grid to determine whether thereference analyte value is an outlier based on clinical acceptability,such as described in more detail with reference U.S. Publication No.US-2005-0027463-A1. In some embodiments, outlier detection compares areference analyte value with a corresponding estimated analyte value,such as described in more detail elsewhere herein and with reference tothe above-described patent application, and the matched data isevaluated using statistical, clinical, and/or physiological parametersto determine the acceptability of the matched data pair. In alternativeembodiments, outlier detection can be determined by other clinical,statistical, and/or physiological boundaries.

In some embodiments, outlier detection utilizes signal artifactsdetection, described in more detail elsewhere herein, to determine thereliability of the reference data and/or sensor data responsive to theresults of the signal artifacts detection. For example, if a certainlevel of signal artifacts is not detected in the data signal, then thesensor data is determined to be reliable. As another example, if acertain level of signal artifacts are detected in the data signal, thenthe reliability of the reference glucose data if the signal artifact isdetermined.

At block 53, a data matching module, also referred to as the processormodule, matches reference data (e.g., one or more reference glucose datapoints) with substantially time corresponding sensor data (e.g., one ormore sensor data points) to provide one or more matched data pairs. Inone embodiment, one reference data point is matched to one timecorresponding sensor data point to form a matched data pair. In anotherembodiment, a plurality of reference data points are averaged (e.g.,equally or non-equally weighted average, mean-value, median, and thelike) and matched to one time corresponding sensor data point to form amatched data pair. In another embodiment, one reference data point ismatched to a plurality of time corresponding sensor data points averagedto form a matched data pair. In yet another embodiment, a plurality ofreference data points are averaged and matched to a plurality of timecorresponding sensor data points averaged to form a matched data pair.

In one embodiment, a time corresponding sensor data comprises one ormore sensor data points that occur, for example, 15±5 min after thereference glucose data timestamp (e.g., the time that the referenceglucose data is obtained). In this embodiment, the minute time delay hasbeen chosen to account for an approximately 10 minute delay introducedby the filter used in data smoothing and an approximately 5 minutediffusional time-lag (e.g., the time necessary for the glucose todiffusion through a membrane(s) of a glucose sensor). In alternativeembodiments, the time corresponding sensor value can be more or lessthan in the above-described embodiment, for example ±60 minutes.Variability in time correspondence of sensor and reference data can beattributed to, for example, a longer or shorter time delay introducedduring signal estimation, or if the configuration of the glucose sensorincurs a greater or lesser physiological time lag.

In some practical implementations of the sensor, the reference glucosedata can be obtained at a time that is different from the time that thedata is input into the receiver 30. Accordingly, it should be noted thatthe “time stamp” of the reference glucose (e.g., the time at which thereference glucose value was obtained) may not be the same as the time atwhich the receiver 30 obtained the reference glucose data. Therefore,some embodiments include a time stamp requirement that ensures that thereceiver 30 stores the accurate time stamp for each reference glucosevalue, that is, the time at which the reference value was actuallyobtained from the user.

In some embodiments, tests are used to evaluate the best-matched pairusing a reference data point against individual sensor values over apredetermined time period (e.g., about 30 minutes). In one suchembodiment, the reference data point is matched with sensor data pointsat 5-minute intervals and each matched pair is evaluated. The matchedpair with the best correlation can be selected as the matched pair fordata processing. In some alternative embodiments, matching a referencedata point with an average of a plurality of sensor data points over apredetermined time period can be used to form a matched pair.

In some embodiments wherein the data signal is evaluated for signalartifacts, as described in more detail elsewhere herein, the processormodule is configured to form a matching data pair only if a signalartifact is not detected. In some embodiments wherein the data signal isevaluated for signal artifacts, the processor module is configured toprompt a user for a reference glucose value during a time when one ormore signal artifact(s) is not detected.

At block 54, a calibration set module, also referred to as the processormodule, forms an initial calibration set from a set of one or morematched data pairs, which are used to determine the relationship betweenthe reference glucose data and the sensor glucose data, such asdescribed in more detail with reference to block 55, below.

The matched data pairs, which make up the initial calibration set, canbe selected according to predetermined criteria. In some embodiments,the number (n) of data pair(s) selected for the initial calibration setis one. In other embodiments, n data pairs are selected for the initialcalibration set wherein n is a function of the frequency of the receivedreference data points. In one exemplary embodiment, six data pairs makeup the initial calibration set. In another embodiment, the calibrationset includes only one data pair.

In some embodiments, the data pairs are selected only within a certainglucose value threshold, for example wherein the reference glucose valueis between about 40 and about 400 mg/dL. In some embodiments, the datapairs that form the initial calibration set are selected according totheir time stamp. In certain embodiments, the data pairs that form theinitial calibration set are selected according to their time stamp, forexample, by waiting a predetermined “break-in” time period afterimplantation, the stability of the sensor data can be increased. Incertain embodiments, the data pairs that form the initial calibrationset are spread out over a predetermined time period, for example, aperiod of two hours or more. In certain embodiments, the data pairs thatform the initial calibration set are spread out over a predeterminedglucose range, for example, spread out over a range of at least 90 mg/dLor more.

In some embodiments, wherein the data signal is evaluated for signalartifacts, as described in more detail elsewhere herein, the processormodule is configured to utilize the reference data for calibration ofthe glucose sensor only if a signal artifact is not detected.

At block 55, the conversion function module, also referred to as theprocessor module, uses the calibration set to create a conversionfunction. The conversion function substantially defines the relationshipbetween the reference glucose data and the glucose sensor data. Avariety of known methods can be used with the preferred embodiments tocreate the conversion function from the calibration set. In oneembodiment, wherein a plurality of matched data points form the initialcalibration set, a linear least squares regression is performed on theinitial calibration set such as described in more detail with referenceto FIG. 6.

At block 56, a sensor data transformation module, also referred to asthe processor module, uses the conversion function to transform sensordata into substantially real-time glucose value estimates, also referredto as calibrated data, or converted sensor data, as sensor data iscontinuously (or intermittently) received from the sensor. For example,the sensor data, which can be provided to the receiver in “counts,” istranslated in to estimate analyte value(s) in mg/dL. In other words, theoffset value at any given point in time can be subtracted from the rawvalue (e.g., in counts) and divided by the slope to obtain the estimatedglucose value:

${{mg}\text{/}{dL}} = \frac{\left( {{rawvalue} - {offset}} \right)}{slope}$

In some alternative embodiments, the sensor and/or reference glucosevalues are stored in a database for retrospective analysis.

At block 57, an output module, also referred to as the processor module,provides output to the user via the user interface. The output isrepresentative of the estimated glucose value, which is determined byconverting the sensor data into a meaningful glucose value such asdescribed in more detail with reference to block 56, above. User outputcan be in the form of a numeric estimated glucose value, an indicationof directional trend of glucose concentration, and/or a graphicalrepresentation of the estimated glucose data over a period of time, forexample. Other representations of the estimated glucose values are alsopossible, for example audio and tactile.

In one embodiment, such as shown in FIG. 3A, the estimated glucose valueis represented by a numeric value. In other exemplary embodiments, suchas shown in FIGS. 3B to 3D, the user interface graphically representsthe estimated glucose data trend over predetermined a time period (e.g.,one, three, and nine hours, respectively). In alternative embodiments,other time periods can be represented. In alternative embodiments, othertime periods can be represented. In alternative embodiments, pictures,animation, charts, graphs, ranges of values, and numeric data can beselectively displayed.

Accordingly, after initial calibration of the sensor, real-timecontinuous glucose information can be displayed on the user interface sothat the user can regularly and proactively care for his/her diabeticcondition within the bounds set by his/her physician.

In alternative embodiments, the conversion function is used to predictglucose values at future points in time. These predicted values can beused to alert the user of upcoming hypoglycemic or hyperglycemic events.Additionally, predicted values can be used to compensate for a time lag(e.g., 15 minute time lag such as described elsewhere herein), if any,so that an estimated glucose value displayed to the user represents theinstant time, rather than a time delayed estimated value.

In some embodiments, the substantially real-time estimated glucosevalue, a predicted future estimated glucose value, a rate of change,and/or a directional trend of the glucose concentration is used tocontrol the administration of a constituent to the user, including anappropriate amount and time, in order to control an aspect of the user'sbiological system. One such example is a closed loop glucose sensor andinsulin pump, wherein the glucose data (e.g., estimated glucose value,rate of change, and/or directional trend) from the glucose sensor isused to determine the amount of insulin, and time of administration,that can be given to a diabetic user to evade hyper- and hypoglycemicconditions.

FIG. 6 is a graph that illustrates one embodiment of a regressionperformed on a calibration set to create a conversion function such asdescribed with reference to FIG. 5, block 55, above. In this embodiment,a linear least squares regression is performed on the initialcalibration set. The x-axis represents reference glucose data; they-axis represents sensor data. The graph pictorially illustratesregression of matched pairs 66 in the calibration set. The regressioncalculates a slope 62 and an offset 64, for example, using thewell-known slope-intercept equation (y=m×+b), which defines theconversion function.

In alternative embodiments, other algorithms could be used to determinethe conversion function, for example forms of linear and non-linearregression, for example fuzzy logic, neural networks, piece-wise linearregression, polynomial fit, genetic algorithms, and other patternrecognition and signal estimation techniques.

In yet other alternative embodiments, the conversion function cancomprise two or more different optimal conversions because an optimalconversion at any time is dependent on one or more parameters, such astime of day, calories consumed, exercise, or glucose concentration aboveor below a set threshold, for example. In one such exemplary embodiment,the conversion function is adapted for the estimated glucoseconcentration (e.g., high vs. low). For example in an implantableglucose sensor it has been observed that the cells surrounding theimplant will consume at least a small amount of glucose as it diffusestoward the glucose sensor. Assuming the cells consume substantially thesame amount of glucose whether the glucose concentration is low or high,this phenomenon will have a greater effect on the concentration ofglucose during low blood sugar episodes than the effect on theconcentration of glucose during relatively higher blood sugar episodes.Accordingly, the conversion function can be adapted to compensate forthe sensitivity differences in blood sugar level. In one implementation,the conversion function comprises two different regression lines,wherein a first regression line is applied when the estimated bloodglucose concentration is at or below a certain threshold (e.g., 150mg/dL) and a second regression line is applied when the estimated bloodglucose concentration is at or above a certain threshold (e.g., 150mg/dL). In one alternative implementation, a predetermined pivot of theregression line that forms the conversion function can be applied whenthe estimated blood is above or below a set threshold (e.g., 150 mg/dL),wherein the pivot and threshold are determined from a retrospectiveanalysis of the performance of a conversion function and its performanceat a range of glucose concentrations. In another implementation, theregression line that forms the conversion function is pivoted about apoint in order to comply with clinical acceptability standards (e.g.,Clarke Error Grid, Consensus Grid, mean absolute relative difference, orother clinical cost function). Although only a few exampleimplementations are described, other embodiments include numerousimplementations wherein the conversion function is adaptively appliedbased on one or more parameters that can affect the sensitivity of thesensor data over time.

Additional methods for processing sensor glucose data are disclosed inU.S. Publication No. US-2005-0027463-A1. In view of the above-describeddata processing, it should be obvious that improving the accuracy of thedata stream will be advantageous for improving output of glucose sensordata. Accordingly, the following description is related to improvingdata output by decreasing signal artifacts on the raw data stream fromthe sensor. The data smoothing methods of preferred embodiments can beemployed in conjunction with any sensor or monitor measuring levels ofan analyte in vivo, wherein the level of the analyte fluctuates overtime, including but not limited to such sensors as described in U.S.Pat. No. 6,001,067 to Shults et al.; U.S. Patent Application2003/0023317 to Brauker et al.; U.S. Pat. No. 6,212,416 to Ward et al.;U.S. Pat. No. 6,119,028 to Schulman et al; U.S. Pat. No. 6,400,974 toLesho; U.S. Pat. No. 6,595,919 to Berner et al.; U.S. Pat. No. 6,141,573to Kurnik et al.; U.S. Pat. No. 6,122,536 to Sun et al.; European PatentApplication EP 1153571 to Varall et al.; U.S. Pat. No. 6,512,939 toColvin et al.; U.S. Pat. No. 5,605,152 to Slate et al.; U.S. Pat. No.4,431,004 to Bessman et al.; U.S. Pat. No. 4,703,756 to Gough et al;U.S. Pat. No. 6,514,718 to Heller et al; and U.S. Pat. No. 5,985,129 toGough et al.

Signal Artifacts

Typically, a glucose sensor produces a data stream that is indicative ofthe glucose concentration of a host, such as described in more detailabove. However, it is well known that the above described glucosesensors includes only a few examples of an abundance of glucose sensorsthat are able to provide raw data output indicative of the concentrationof glucose. Thus, it should be understood that the systems and methodsdescribed herein, including signal artifacts detection, signal artifactsreplacement, and other data processing, can be applied to a data streamobtained from any glucose sensor.

Raw data streams typically have some amount of “system noise,” caused byunwanted electronic or diffusion-related noise that degrades the qualityof the signal and thus the data. Accordingly, conventional glucosesensors are known to smooth raw data using methods that filter out thissystem noise, and the like, in order to improve the signal to noiseratio, and thus data output. One example of a conventionaldata-smoothing algorithm includes a finite impulse response filter(FIR), which is particularly suited for reducing high-frequency noise(see Steil et al. U.S. Pat. No. 6,558,351).

FIGS. 7A and 7B are graphs of raw data streams from an implantableglucose sensor prior to data smoothing. FIG. 7A is a graph that shows araw data stream obtained from a glucose sensor over an approximately 4hour time span in one example. FIG. 7B is a graph that shows a raw datastream obtained from a glucose sensor over an approximately 36 hour timespan in another example. The x-axis represents time in minutes. They-axis represents sensor data in counts. In these examples, sensoroutput in counts is transmitted every 30-seconds.

The “system noise” such as shown in sections 72 a, 72 b of the datastreams of FIGS. 7A and 7B, respectively, illustrate time periods duringwhich system noise can be seen on the data stream. This system noise canbe characterized as Gaussian, Brownian, and/or linear noise, and can besubstantially normally distributed about the mean. The system noise islikely electronic and diffusion-related, and the like, and can besmoothed using techniques such as by using an FIR filter. As anotherexample, the raw data can be represented by an integrated value, forexample, by integrating the signal over a time period (e.g., 30 secondsor 5 minutes), and providing an averaged (e.g., integrated) data pointthere from. The system noise such as shown in the data of sections 72 a,72 b is a fairly accurate representation of glucose concentration andcan be confidently used to report glucose concentration to the user whenappropriately calibrated.

The “signal artifacts” such as shown in sections 74 a, 74 b of the datastream of FIGS. 7A and 7B, respectively, illustrate time periods duringwhich “signal artifacts” can be seen, which are significantly differentfrom the previously described system noise (sections 72 a, 72 b). Thisnoise, such as shown in section 74 a and 74 b, is referred to herein as“signal artifacts” and may be described as “transient non-glucosedependent signal artifacts that have a higher amplitude than systemnoise.” At times, signal artifacts comprise low noise, which generallyrefers to noise that substantially decreases signal amplitude 76 a, 76 bherein, which is best seen in the signal artifacts 74 b of FIG. 7B.Occasional high spikes 78 a, 78 b, which generally correspond to noisethat substantially increases signal amplitude, can also be seen in thesignal artifacts, which generally occur after a period of low noise.These high spikes are generally observed after transient low noise andtypically result after reaction rate-limiting phenomena occur. Forexample, in an embodiment where a glucose sensor requires an enzymaticreaction, local ischemia creates a reaction that is rate-limited byoxygen, which is responsible for low noise. In this situation, glucosewould be expected to build up in the membrane because it would not becompletely catabolized during the oxygen deficit. When oxygen is againin excess, there would also be excess glucose due to the transientoxygen deficit. The enzyme rate would speed up for a short period untilthe excess glucose is catabolized, resulting in high noise.Additionally, noise can be distributed both above and below the expectedsignal.

Analysis of signal artifacts such as shown sections 74 a, 74 b of FIGS.7A and 7B, respectively, indicates that the observed low noise is causedby substantially non-glucose reaction dependent phenomena, such asischemia that occurs within or around a glucose sensor in vivo, forexample, which results in the reaction becoming oxygen dependent. As afirst example, at high glucose levels, oxygen can become limiting to theenzymatic reaction, resulting in a non-glucose dependent downward trendin the data (best seen in FIG. 7B). As a second example, certainmovements or postures taken by the patient can cause transient downwardnoise as blood is squeezed out of the capillaries resulting in localischemia, and causing non-glucose dependent low noise. Because excessoxygen (relative to glucose) is necessary for proper sensor function,transient ischemia can result in a loss of signal gain in the sensordata. In this second example oxygen can also become transiently limiteddue to contracture of tissues around the sensor interface. This issimilar to the blanching of skin that can be observed when one putspressure on it. Under such pressure, transient ischemia can occur inboth the epidermis and subcutaneous tissue. Transient ischemia is commonand well tolerated by subcutaneous tissue.

In another example of non-glucose reaction rate-limiting phenomena, skintemperature can vary dramatically, which can result in thermally relatederosion of the signal (e.g., temperature changes between 32 and 39degrees Celsius have been measured in humans). In yet anotherembodiment, wherein the glucose sensor is placed intravenously,increased impedance can result from the sensor resting against wall ofthe blood vessel, for example, producing this non-glucose reactionrate-limiting noise due to oxygen deficiency.

Because signal artifacts are not mere system noise, but rather arecaused by known or unknown non-glucose related mechanisms, methods usedfor conventional random noise filtration produce data lower (or in somecases higher) than the actual blood glucose levels due to the expansivenature of these signal artifacts. To overcome this, the preferredembodiments provide systems and methods for replacing at least some ofthe signal artifacts by estimating glucose signal values.

FIG. 8 is a flow chart that illustrates the process of detecting andreplacing signal artifacts in certain embodiments. It is noted that“signal artifacts” particularly refers to the transient non-glucoserelated artifacts such as described in more detail elsewhere herein.Typically, signal artifacts are caused by non-glucose rate-limitingphenomenon such as described in more detail above.

At block 82, a sensor data receiving module, also referred to as thesensor data module 82, or processor module, receives sensor data (e.g.,a data stream), including one or more time-spaced sensor data points. Insome embodiments, the data stream is stored in the sensor for additionalprocessing; in some alternative embodiments, the sensor periodicallytransmits the data stream to the receiver 30, which can be in wired orwireless communication with the sensor. In some embodiments, raw and/orfiltered data is stored in the sensor and/or receiver.

At block 84, a signal artifacts detection module, also referred to asthe signal artifacts detector 84 or signal reliability module, isprogrammed to detect transient non-glucose related signal artifacts inthe data stream, such as described in more detail with reference toFIGS. 7A and 7B, above. The signal artifacts detector can comprise anoxygen detector, a pH detector, a temperature detector, and/or apressure/stress detector, for example, the signal artifacts detector 29in FIG. 2. In some embodiments, the signal artifacts detector at block84 is located within the processor 22 in FIG. 2 and utilizes existingcomponents of the glucose sensor to detect signal artifacts, for exampleby pulsed amperometric detection, counter electrode monitoring,reference electrode monitoring, and frequency content monitoring, whichare described elsewhere herein. In yet other embodiments, the datastream can be sent from the sensor to the receiver which comprisesprogramming in the processor 42 in FIG. 4 that performs algorithms todetect signal artifacts, for example such as described with reference to“Cone of Possibility Detection” method and/or by comparing raw data vs.filtered data, both of which are described in more detail below.Numerous embodiments for detecting signal artifacts are described inmore detail in the section entitled, “Signal Artifacts Detection,” allof which are encompassed by the signal artifacts detection at block 84.

In certain embodiments, the processor module in either the sensorelectronics and/or the receiver electronics can evaluate an intermittentor continuous signal-to-noise measurement to determine aberrancy ofsensor data responsive to a signal-to-noise ratio above a set threshold.In certain embodiments, signal residuals (e.g., by comparing raw andfiltered data) can be intermittently or continuously analyzed for noiseabove a set threshold. In certain embodiments, pattern recognition canbe used to identify noise associated with physiological conditions, suchas low oxygen, or other known signal aberrancies. Accordingly, in theseembodiments, the system can be configured, in response to aberrancies inthe data stream, to trigger signal estimation, adaptively filter thedata stream according to the aberrancy, and the like, as described inmore detail elsewhere herein.

At block 86, the signal artifacts replacement module, also referred toas the signal estimation module, replaces some or an entire data streamwith estimated glucose signal values using signal estimation. Numerousembodiments for performing signal estimation are described in moredetail in the section entitled “Signal Artifacts Replacement,” all ofwhich are encompassed by the signal artifacts replacement module, block86. It is noted that in some embodiments, signal estimation/replacementis initiated in response to positive detection of signal artifacts onthe data stream, and subsequently stopped in response to detection ofnegligible signal artifacts on the data stream. In some embodiments, thesystem waits a predetermined time period (e.g., between 30 seconds and30 minutes) before switching the signal estimation on or off to ensurethat a consistent detection has been ascertained. In some embodiments,however, signal estimation/replacement can continuously or continuallyrun.

Some embodiments of signal estimation can additionally includediscarding data that is considered sufficiently unreliable and/orerroneous such that the data should not be used in a signal estimationalgorithm. In these embodiments, the system can be programmed to discardoutlier data points, for example data points that are so extreme thatthey can skew the data even with the most comprehensive filtering orsignal estimation, and optionally replace those points with a projectedvalue based on historical data or present data (e.g., linear regression,recursive filtering, and the like). One example of discarding sensordata includes discarding sensor data that falls outside of a “Cone ofPossibility” such as described in more detail elsewhere herein. Anotherexample includes discarding sensor data when signal artifacts detectiondetects values outside of a predetermined threshold (e.g., oxygenconcentration below a set threshold, temperature above a certainthreshold, signal amplitude above a certain threshold, etc). Any of thesignal estimation/replacement algorithms described herein can then beused to project data values for those data that were discarded.

Signal Artifacts Detection

Analysis of signals from glucose sensors indicates at least two types ofnoise, which are characterized herein as 1) system noise and 2) signalartifacts, such as described in more detail above. It is noted thatsystem noise is easily smoothed using the algorithms provided herein;however, the systems and methods described herein particularly addresssignal artifacts, by replacing transient erroneous signal noise causedby rate-limiting phenomenon (e.g., non-glucose related signal) withestimated signal values, for example.

In certain embodiments of signal artifacts detection, oxygen monitoringis used to detect whether transient non-glucose dependent signalartifacts due to ischemia. Low oxygen concentrations in or near theglucose sensor can account for a large part of the transient non-glucoserelated signal artifacts as defined herein on a glucose sensor signal,particularly in subcutaneously implantable glucose sensors. Accordingly,detecting oxygen concentration, and determining if ischemia exists candiscover ischemia-related signal artifacts. A variety of methods can beused to test for oxygen. For example, an oxygen-sensing electrode, orother oxygen sensor can be employed. The measurement of oxygenconcentration can be sent to a processor, which determines if the oxygenconcentration indicates ischemia.

In some embodiments of ischemia detection, an oxygen sensor is placedproximal to or within the glucose sensor. For example, the oxygen sensorcan be located on or near the glucose sensor such that their respectivelocal environments are shared and oxygen concentration measurement fromthe oxygen sensor represents an accurate measurement of the oxygenconcentration on or within the glucose sensor. In some alternativeembodiments of ischemia detection, an oxygen sensor is also placeddistal to the glucose sensor. For example, the oxygen sensor can belocated sufficiently far from the glucose sensor such that theirrespective local environments are not shared and oxygen measurementsfrom the proximal and distal oxygen sensors can be compared to determinethe relative difference between the respective local environments. Bycomparing oxygen concentration at proximal and distal oxygen sensors,change in local (proximal) oxygen concentration can be determined from areference (distal) oxygen concentration.

Oxygen sensors are useful for a variety of purposes. For example, U.S.Pat. No. 6,512,939 to Colvin et al., which is incorporated herein byreference, discloses an oxygen sensor that measures background oxygenlevels. However, Colvin et al. rely on the oxygen sensor for the datastream of glucose measurements by subtraction of oxygen remaining afterexhaustion of glucose by an enzymatic reaction from total unreactedoxygen concentration.

In another embodiment of ischemia detection, wherein the glucose sensoris an electrochemical sensor that includes a potentiostat, pulsedamperometric detection can be employed to determine an oxygenmeasurement. Pulsed amperometric detection includes switching, cycling,or pulsing the voltage of the working electrode (or reference electrode)in an electrochemical system, for example between a positive voltage(e.g., +0.6 for detecting glucose) and a negative voltage (e.g., −0.6for detecting oxygen). U.S. Pat. No. 4,680,268 to Clark, Jr., which isincorporated by reference herein, describes pulsed amperometricdetection. In contrast to using signal replacement, Clark, Jr. addressesoxygen deficiency by supplying additional oxygen to the enzymaticreaction.

In another embodiment of ischemia detection, wherein the glucose sensoris an electrochemical sensor and contains a potentiostat, oxygendeficiency can be seen at the counter electrode when insufficient oxygenis available for reduction, which thereby affects the counter electrodein that it is unable to balance the current coming from the workingelectrode. When insufficient oxygen is available for the counterelectrode, the counter electrode can be driven in its electrochemicalsearch for electrons all the way to its most negative value, which couldbe ground or 0.0V, which causes the reference to shift, reducing thebias voltage such as described in more detail below. In other words, acommon result of ischemia will be seen as a drop off in sensor currentas a function of glucose concentration (e.g., lower sensitivity). Thishappens because the working electrode no longer oxidizes all of the H₂O₂arriving at its surface because of the reduced bias. In some extremecircumstances, an increase in glucose can produce no increase in currentor even a decrease in current.

FIG. 9 is a graph that shows a comparison of sensor current andcounter-electrode voltage in a host over time. The x-axis representstime in minutes. The first y-axis 91 represents sensor counts from theworking electrode and thus plots a raw sensor data stream 92 for theglucose sensor over a period of time. The second y-axis 93 representscounter-electrode voltage 94 in counts. The graph illustrates thecorrelation between sensor data 92 and counter-electrode voltage 94;particularly, that erroneous counter electrode function 96 where thecounter voltages drops approximately to zero substantially coincideswith transient non-glucose related signal artifacts 98. In other words,when counter-electrode voltage is at or near zero, sensor data includessignal artifacts.

In another embodiment of ischemia detection, wherein the glucose sensoris an electrochemical sensor and contains a two- or three-cellelectrochemical cell, signal artifacts are detected by monitoring thereference electrode. This “reference drift detection” embodiment takesadvantage of the fact that the reference electrode will vary or drift inorder to maintain a stable bias potential with the working electrode,such as described in more detail herein. This “drifting” generallyindicates non-glucose reaction rate-limiting noise, for example due toischemia. It is noted that the following example describes an embodimentwherein the sensor includes a working, reference, and counterelectrodes, such as described in more detail elsewhere herein; howeverthe principles of this embodiment are applicable to a two-cell (e.g.,anode and cathode) electrochemical cell as is understood in the art.

FIG. 10A is a circuit diagram of a potentiostat that controls a typicalthree-electrode system, which can be employed with a glucose sensor suchas described with reference to FIGS. 1 and 2. The potentiostat includesa working electrode 100, a reference electrode 102, and a counterelectrode 104. The voltage applied to the working electrode is aconstant value (e.g., +1.2V) and the voltage applied to the referenceelectrode is also set at a constant value (e.g., +0.6V) such that thepotential (V_(BIAS)) applied between the working and referenceelectrodes is maintained at a constant value (e.g., +0.6V). The counterelectrode is configured to have a constant current (equal to the currentbeing measured by the working electrode), which is accomplished byvarying the voltage at the counter electrode in order to balance thecurrent going through the working electrode 100 such that current doesnot pass through the reference electrode 102. A negative feedback loop107 is constructed from an operational amplifier (OP AMP), the referenceelectrode 102, the counter electrode 104, and a reference potential, tomaintain the reference electrode at a constant voltage.

In practice, a glucose sensor of one embodiment comprises a membranethat contains glucose oxidase that catalyzes the conversion of oxygenand glucose to hydrogen peroxide and gluconate, such as described withreference to FIGS. 1 and 2. Therefore, for each glucose moleculemetabolized there is a change equivalent in molecular concentration inthe co-reactant O₂ and the product H₂O₂. Consequently, one can use anelectrode (e.g., working electrode 100) to monitor theconcentration-induced current change in either the co-reactant or theproduct to determine glucose concentration.

One limitation of the electrochemistry is seen when insufficientnegative voltage is available to the counter electrode 104 to balancethe working electrode 100. This limitation can occur when insufficientoxygen is available to the counter electrode 104 for reduction, forexample. When this limitation occurs, the counter electrode can nolonger vary its voltage to maintain a balanced current with the workingelectrode and thus the negative feedback loop 107 used to maintain thereference electrode is compromised. Consequently, the referenceelectrode voltage will change or “drift,” altering the applied biaspotential (i.e., the potential applied between the working and referenceelectrodes), thereby decreasing the applied bias potential. When thischange in applied bias potential occurs, the working electrode canproduce erroneous glucose measurements due to either increased ordecreased signal strength (I_(SENSE)).

FIG. 10B a diagram referred to as Cyclic-Voltammetry (CV) curve, whereinthe x-axis represents the applied potential (V_(BIAS)) and the y-axisrepresents the signal strength of the working electrode (I_(SENSE)). Acurve 108 illustrates an expected CV curve when the potentiostat isfunctioning normally. Typically, desired bias voltage can be set (e.g.,V_(BIAS1)) and a resulting current will be sensed (e.g., I_(SENSE1)). Asthe voltage decreases (e.g., V_(BIAS2)) due to reference voltage drift,for example, a new resulting current is sensed (e.g., I_(SENSE2)).Therefore, the change in bias is an indicator of signal artifacts andcan be used in signal estimation and to replace the erroneous resultingsignals. In addition to ischemia, the local environment at the electrodesurfaces can affect the CV curve, for example, changes in pH,temperature, and other local biochemical species can significantly alterthe location of the CV curve.

FIG. 10C is a CV curve that illustrates an alternative embodiment ofsignal artifacts detection, wherein pH and/or temperature can bemonitoring using the CV curve and diagnosed to detect transientnon-glucose related signal artifacts. For example, signal artifacts canbe attributed to thermal changes and/or pH changes in some embodimentsbecause certain changes in pH and temperature affect data from a glucosesensor that relies on an enzymatic reaction to measure glucose. Signalartifacts caused by pH changes, temperature changes, changes inavailable electrode surface area, and other local biochemical speciescan be detected and signal estimation can be applied an/or optimizedsuch as described in more detail elsewhere herein. In FIG. 10C, a firstcurve 108 illustrates an expected CV curve when the potentiostat isfunctioning normally. A second curve 109 illustrates a CV curve whereinthe environment has changed as indicated by the upward shift of the CVcurve.

In some embodiments, pH and/or temperature measurements are obtainedproximal to the glucose sensor; in some embodiments, pH and/ortemperature measurements are also obtained distal to the glucose sensorand the respective measurements compared, such as described in moredetail above with reference to oxygen sensors.

In another implementation of signal artifacts detection, whereintemperature is detected, an electronic thermometer can be proximal to orwithin the glucose sensor, such that the temperature measurement isrepresentative of the temperature of the glucose sensor's localenvironment. It is noted that accurate sensor function depends ondiffusion of molecules from the blood to the interstitial fluid, andthen through the membranes of the device to the enzyme membrane.Additionally, diffusion transport of hydrogen peroxide from the enzymemembrane to the electrode is required for accurate sensor function insome embodiments. Therefore, temperatures can be a rate determiningparameter of diffusion. As temperature decreases, diffusion transportdecreases. Under certain human conditions, such as hypothermia or fever,the variations can be considerably greater. Additionally, under normalconditions, the temperature of subcutaneous tissue is known to varyconsiderably more than core tissues (e.g., core temperature).Temperature thresholds can be set to detect signal artifactsaccordingly.

In another implementation, a pH detector is used to detect signalartifacts. In glucose sensors that rely on enzymatic reactions, a pH ofthe fluid to be sensed can be within the range of about 5.5 to 7.5.Outside of this range, effects may be seen in the enzymatic reaction andtherefore data output of the glucose sensor. Accordingly, by detectingif the pH is outside of a predetermined range (e.g., 5.5 to 7.5), a pHdetector may detect transient non-glucose related signal artifacts suchas described herein. It is noted that the pH threshold can be set atranges other than provided herein without departing from the preferredembodiments.

In an alternative embodiment of signal artifacts detection, pressureand/or stress can be monitored using known techniques for example by astrain gauge placed on the sensor that detects stress/strain on thecircuit board, sensor housing, or other components. A variety ofmicroelectromechanical systems (MEMS) can be utilized to measurepressure and/or stress within the sensor.

In another alternative embodiment of signal artifacts detection, theprocessor in the sensor (or receiver) periodically evaluates the datastream for high amplitude noise, which is defined by noisy data whereinthe amplitude of the noise is above a predetermined threshold. Forexample, in the graph of FIGS. 7A and 7B, the system noise sections suchas 72 a and 72 b have a substantially low amplitude noise threshold; incontrast to system noise, signal artifacts sections such as 74 a and 74b have signal artifacts (noise) with an amplitude that is much higherthan that of system noise. Therefore, a threshold can be set at or abovethe amplitude of system noise, such that when noisy data is detectedabove that amplitude, it can be considered “signal artifacts” as definedherein.

In another alternative embodiment of signal artifacts detection, amethod hereinafter referred to as the “Cone of Possibility DetectionMethod,” utilizes physiological information along with glucose signalvalues in order define a “cone” of physiologically feasible glucosesignal values within a human, such that signal artifacts are detectedwhenever the glucose signal falls outside of the cone of possibility.Particularly, physiological information depends upon the physiologicalparameters obtained from continuous studies in the literature as well asour own observations. A first physiological parameter uses a maximalsustained rate of change of glucose in humans (e.g., about 4 to 5mg/dL/min) and a maximum acceleration of that rate of change (e.g.,about 0.1 to 0.2 mg/dL/min²). A second physiological parameter uses theknowledge that rate of change of glucose is lowest at the minima, whichis the areas of greatest risk in patient treatment, and the maxima,which has the greatest long-term effect on secondary complicationsassociated with diabetes. A third physiological parameter uses the factthat the best solution for the shape of the curve at any point along thecurve over a certain time period (e.g., about 20-30 minutes) is astraight line. Additional physiological parameters can be incorporatedand are within the scope of this embodiment.

In practice, the Cone of Possibility Detection Method combines any oneor more of the above-described physiological parameters to form analgorithm that defines a cone of possible glucose levels for glucosedata captured over a predetermined time period. In one exemplaryimplementation of the Cone of Possibility Detection Method, the system(processor in the sensor or receiver) calculates a maximum physiologicalrate of change and determines if the data falls within thesephysiological limits; if not, signal artifacts are identified. It isnoted that the maximum rate of change can be narrowed (e.g., decreased)in some instances. Therefore, additional physiological data could beused to modify the limits imposed upon the Cone of PossibilitiesDetection Method for sensor glucose values. For example, the maximum perminute rate change can be lower when the subject is sleeping or hasn'teaten in eight hours; on the other hand, the maximum per minute ratechange can be higher when the subject is exercising or has consumed highlevels of glucose, for example. In general, it has been observed thatrates of change are slowest near the maxima and minima of the curve, andthat rates of change are highest near the midpoint between the maximaand minima. It should further be noted that rate of change limits arederived from analysis of a range of data significantly higherunsustained rates of change can be observed.

In another alternative embodiment of signal artifacts detection,examination of the spectral content (e.g., frequency content) of thedata stream can yield measures useful in detecting signal artifacts. Forexample, data that has high frequency, and in some cases can becharacterized by a large negative slope, are indicative of signalartifacts and can cause sensor signal loss. Specific signal content canbe monitored using an orthogonal transform, for example a Fouriertransform, a Discrete Fourier Transform (DFT), or any other method knownin the art.

FIG. 11 is a graph of 110 a raw data stream from a glucose sensor and aspectrogram 114 that shows the frequency content of the raw data streamin one embodiment. Particularly, the graph 110 illustrates the raw datastream 112 and includes an x-axis that represents time in hours and ay-axis that represents sensor data output in counts; the spectrogram 114illustrates the frequency content 116 corresponding to the raw datastream 112 and includes an x-axis that represents time in hourscorresponding to the x-axis of the graph 110 and a y-axis thatrepresents frequency content in cycles per hour. The darkness of eachpoint represents the amplitude of that frequency at that time. Darkerpoints relate to higher amplitudes. Frequency content on the spectrogram114 was obtained using a windowed Discrete Fourier transform.

The raw data stream in the graph 110 has been adjusted by a linearmapping similar to the calibration algorithm. In this example, the bias(or intercept) has been adjusted but not the proportion (or slope). Theslope of the raw data stream would typically be determined by somecalibration, but since the calibration has not occurred in this example,the gray levels in the spectrogram 114 indicate relative values. Thelower values of the graph 110 are white. They are colored as white belowa specific value, highlighting only the most intense areas of the graph.

By monitoring the frequency content 116, high frequency cycles 118 canbe observed. The high frequency cycles 118 correspond to signalartifacts 119 such as described herein. Thus, signal artifacts can bedetected on a data stream by monitoring frequency content and setting athreshold (e.g., 30 cycles per hour). The set threshold can varydepending on the signal source.

In another alternative embodiment of signal artifacts detection,examination of the signal information content can yield measures usefulin detecting signal artifacts. Time series analysis can be used tomeasure entropy, approximate entropy, variance, and/or percent change ofthe information content over consecutive windows (e.g., 30 and 60 minutewindows of data) of the raw data stream. In one exemplary embodiment,the variance of the raw data signal is measured over 15 minute and 45minute windows, and signal artifacts are detected when the variance ofthe data within the 15-minute window exceeds the variance of the datawithin the 45-minute window. Alternatively, other methods ofself-diagnosis can be performed on the signal to determine a level ofsignal artifacts. One example includes performing a first derivativeanalysis that compares consecutive points, and detects signal artifactswhen point to point changes are above a physiologically feasiblethreshold, for example. Another example of signal self-diagnosisincludes performing a second derivative analysis that considers turningpoints, for example, detects signal artifacts when changes are notsufficiently gradual (e.g., within thresholds), for example.

In yet another alternative embodiment of signal artifacts detection thatutilizes examination or evaluation of the signal information content,filtered (e.g., smoothed) data is compared to raw data (e.g., in sensorelectronics or in receiver electronics). In one such embodiment, asignal residual is calculated as the difference between the filtereddata and the raw data. For example, at one time point (or one timeperiod that is represented by a single raw value and single filteredvalue), the filtered data can be measured at 50,000 counts and the rawdata can be measured at 55,500 counts, which would result in a signalresidual of 5,500 counts. In some embodiments, a threshold can be set(e.g., 5000 counts) that represents a first level of noise (e.g., signalartifact) in the data signal, when the residual exceeds that level.Similarly, a second threshold can be set (e.g., 8,000 counts) thatrepresents a second level of noise in the data signal. Additionalthresholds and/or noise classifications can be defined as is appreciatedby one skilled in the art. Consequently, signal filtering, processing,and/or displaying decisions can be executed based on these conditions(e.g., the predetermined levels of noise).

Although the above-described example illustrates one method ofdetermining a level of noise, or signal artifact(s), based on acomparison of raw vs. filtered data for a time point (or single valuesrepresentative of a time period). In an alternative exemplary embodimentfor determining noise, signal artifacts are evaluated for noise episodeslasting a certain period of time. For example, the processor (in thesensor or receiver) can be configured to look for a certain number ofsignal residuals above a predetermined threshold (representing noisetime points or noisy time periods) for a predetermined period of time(e.g., a few minutes to a few hours or more).

In one exemplary embodiment, a processor is configured to determine asignal residual by subtracting the filtered signal from the raw signalfor a predetermined time period. It is noted that the filtered signalcan be filtered by any known smoothing algorithm such as describedherein, for example a 3-point moving average-type filter. It is furthernoted that the raw signal can include an average value, e.g., whereinthe value is integrated over a predetermined time period (such as5-minutes). Furthermore, it is noted that the predetermined time periodcan be a time point or representative data for a time period (e.g., 5minutes). In some embodiments, wherein a noise episode for apredetermined time period is being evaluated, a differential can beobtained by comparing a signal residual with a previous signal residual(e.g., a residual at time (t)=0 as compared to a residual at (t)-5minutes.) Similar to the thresholds described above with regard to thesignal residual, one or more thresholds can be set for thedifferentials, whereby one or more differentials above one of thepredetermined differential thresholds defines a particular noise level.It has been shown in certain circumstances that a differentialmeasurement as compared to a residual measurement as described herein,amplifies noise and therefore may be a more sensitive to noise episodes.Accordingly, a noise episode, or noise episode level, can be defined byone or more points (e.g., residuals or differentials) above apredetermined threshold, and in some embodiments, for a predeterminedperiod of time. Similarly, a noise level determination can be reduced oraltered when a different (e.g., reduced) number of points above thepredetermined threshold are calculated in a predetermined period oftime.

One or a plurality of the above signal artifacts detection models can beused alone or in combination to detect signal artifacts such asdescribed herein. Accordingly, the data stream associated with thesignal artifacts can be discarded, replaced, or otherwise processed inorder to reduce or eliminate these signal artifacts and thereby improvethe value of the glucose measurements that can be provided to a user.

Signal Artifacts Replacement

Signal Artifacts Replacement, such as described above, can use systemsand methods that reduce or replace these signal artifacts that can becharacterized by transience, high frequency, high amplitude, and/orsubstantially non-linear noise. Accordingly, a variety of filters,algorithms, and other data processing are provided that address thedetected signal artifacts by replacing the data stream, or portion ofthe data stream, with estimated glucose signal values. It is noted that“signal estimation” as used herein, is a broad term, which includesfiltering, data smoothing, augmenting, projecting, and/or otheralgorithmic methods that estimate glucose signal values based on presentand historical data.

It is noted that a glucose sensor can contain a processor and the likethat processes periodically received raw sensor data (e.g., every 30seconds). Although a data point can be available constantly, for exampleby use of an electrical integration system in a chemo-electric sensor,relatively frequent (e.g., every 30 seconds), or less frequent datapoint (e.g., every 5 minutes), can be more than sufficient for patientuse. It is noted that accordingly Nyquist Theory, a data point isrequired about every 10 minutes to accurately describe physiologicalchange in glucose in humans. This represents the lowest useful frequencyof sampling. However, it should be recognized that it is desirable tosample more frequently than the Nyquist minimum, to provide forsufficient data in the event that one or more data points are lost, forexample. Additionally, more frequently sampled data (e.g., 30-second)can be used to smooth the less frequent data (e.g., 5-minute) that aretransmitted. It is noted that in this example, during the course of a5-minute period, 10 determinations are made at 30-second intervals.

In some embodiments of Signal Artifacts Replacement, signal estimationcan be implemented in the sensor and transmitted to a receiver foradditional processing. In some embodiments of Signal ArtifactsReplacement, raw data can be sent from the sensor to a receiver forsignal estimation and additional processing therein. In some embodimentsof Signal Artifacts Replacement, signal estimation is performedinitially in the sensor, with additional signal estimation in thereceiver.

In some embodiments of Signal Artifacts Replacement, wherein the sensoris an implantable glucose sensor, signal estimation can be performed inthe sensor to ensure a continuous stream of data. In alternativeembodiments, data can be transmitted from the sensor to the receiver,and the estimation performed at the receiver; It is noted however thatthere can be a risk of transmit-loss in the radio transmission from thesensor to the receiver when the transmission is wireless. For example,in embodiments wherein a sensor is implemented in vivo, the raw sensorsignal can be more consistent within the sensor (in vivo) than the rawsignal transmitted to a source (e.g., receiver) outside the body (e.g.,if a patient were to take the receiver off to shower, communicationbetween the sensor and receiver can be lost and data smoothing in thereceiver would halt accordingly). Consequently, It is noted that amultiple point data loss in the filter can take for example, about 25 toabout 40 minutes for the data to recover to near where it would havebeen had there been no data loss.

In some embodiments of Signal Artifacts Replacement, signal estimationis initiated only after signal artifacts are positively detected andstopped once signal artifacts are negligibly detected. In somealternative embodiments signal estimation is initiated after signalartifacts are positively detected and then stopped after a predeterminedtime period. In some alternative embodiments, signal estimation can becontinuously or continually performed. In some alternative embodiments,one or more forms of signal estimation can be accomplished based on theseverity of the signal artifacts, such as will be described withreference the section entitled, “Selective Application of SignalArtifacts Replacement.”

In some embodiments of Signal Artifacts Replacement, the processorperforms a linear regression. In one such implementation, the processorperforms a linear regression analysis of the n (e.g., 10) most recentsampled sensor values to smooth out the noise. A linear regressionaverages over a number of points in the time course and thus reduces theinfluence of wide excursions of any point from the regression line.Linear regression defines a slope and intercept, which is used togenerate a “Projected Glucose Value,” which can be used to replacesensor data. This regression can be continually performed on the datastream or continually performed only during the transient signalartifacts. In some alternative embodiments, signal estimation caninclude non-linear regression.

In another embodiment of Signal Artifacts Replacement, the processorperforms a trimmed regression, which is a linear regression of a trimmedmean (e.g., after rejecting wide excursions of any point from theregression line). In this embodiment, after the sensor records glucosemeasurements at a predetermined sampling rate (e.g., every 30 seconds),the sensor calculates a trimmed mean (e.g., removes highest and lowestmeasurements from a data set and then regresses the remainingmeasurements to estimate the glucose value.

FIG. 12 is a graph that illustrates a raw data stream from a glucosesensor and a trimmed regression that can be used to replace some of orthe entire data stream. The x-axis represents time in minutes; they-axis represents sensor data output in counts. A raw data signal 120,which is illustrated as a dotted line, shows a data stream wherein somesystem noise can be detected, however signal artifacts 122 can beparticularly seen in a portion thereof (and can be detected by methodssuch as described above). The trimmed regression line 124, which isillustrated as a solid line, is the data stream after signal estimationusing a trimmed linear regression algorithm, such as described above,and appears at least somewhat “smoothed” on the graph. In thisparticular example, the trimmed regression uses the most recent 60points (30 minutes) and trims out the highest and lowest values, thenuses the leftover 58 points to project the next point. It is noted thatthe trimmed regression 124 provides a good estimate throughout themajority data stream, however is only somewhat effective in smoothingthe data in during signal artifacts 122. To provide an optimizedestimate of the glucose data values, the trimmed regression can beoptimized by changing the parameters of the algorithm, for example bytrimming more or less raw glucose data from the top and/or bottom of thesignal artifacts 122 prior to regression. Additionally, trimmedregression, because of its inherent properties, can be particularlysuited for noise of a certain amplitude and/or characteristic. In oneembodiment, for example trimmed regression can be selectively appliedbased on the severity of the signal artifacts, which is described inmore detail below with reference to FIGS. 15 to 17.

In another embodiment of Signal Artifacts Replacement, the processorruns a non-recursive filter, such as a finite impulse response (FIR)filter. A FIR filter is a digital signal filter, in which every sampleof output is the weighted sum of past and current samples of input,using only some finite number of past samples.

FIG. 13 is a graph that illustrates a raw data stream from a glucosesensor and an FIR-estimated signal that can be used to replace some ofor the entire data stream. The x-axis represents time in minutes; they-axis represents sensor data output in counts. A raw data signal 130,which is illustrated as a dotted line, shows a data stream wherein somesystem noise can be detected, however signal artifacts 132 can beparticularly seen in a portion thereof (and can be detected by methodssuch as described above). The FIR-estimated signal 134, which isillustrated as a solid line, is the data stream after signal estimationusing a FIR filter, such as described above, and appears at leastsomewhat “smoothed” on the graph. It is noted that the FIR-estimatedsignal provides a good estimate throughout the majority of the datastream; however like trimmed regression it is only somewhat effective insmoothing the data during signal artifacts 132. To provide an optimizedestimate of the glucose data values, the FIR filter can be optimized bychanging the parameters of the algorithm, for example the tuning of thefilter, particularly the frequencies of the pass band and the stop band.Additionally, it is noted that the FIR filter, because of its inherentproperties, can be particularly suited for noise of a certain amplitudeand/or characteristic. In one embodiment, for example the FIR filter canbe selectively applied based on the severity of the signal artifacts,which is described in more detail below with reference to FIGS. 15 to17. It is noted that the FIR-estimated signal induces a time lag on thedata stream, which can be increased or decreased in order to optimizethe filtering or to minimize the time lag, for example.

In another embodiment of Signal Artifacts Replacement, the processorruns a recursive filter, such as an infinite impulse response (IIR)filter. An IIR filter is a type of digital signal filter, in which everysample of output is the weighted sum of past and current samples ofinput. In one exemplary implementation of an IIR filter, the output iscomputed using 6 additions/subtractions and 7 multiplications as shownin the following equation:

${y(n)} = \frac{\begin{matrix}{{a_{0}*{x(n)}} + {a_{1}*{x\left( {n - 1} \right)}} + {a_{2}*{x\left( {n - 2} \right)}} + {a_{3}*{x\left( {n - 3} \right)}} -} \\{{b_{1}*{y\left( {n - 1} \right)}} - {b_{2}*{y\left( {n - 2} \right)}} - {b_{3}*{y\left( {n - 3} \right)}}}\end{matrix}}{b_{0}}$This polynomial equation includes coefficients that are dependent onsample rate and frequency behavior of the filter. Frequency behaviorpasses low frequencies up to cycle lengths of 40 minutes, and is basedon a 30 second sample rate. In alternative implementations, the samplerate and cycle lengths can be more or less. See Lynn “Recursive DigitalFilters for Biological Signals” Med. & Biol. Engineering, Vol. 9, pp.37-43, which is incorporated herein by reference in its entirety.

FIG. 14 is a graph that illustrates a raw data stream from a glucosesensor and an IIR-estimated signal that can be used to replace some ofor the entire data stream. The x-axis represents time in minutes; they-axis represents sensor data output in counts. A raw data signal 140,which is illustrated as a dotted line, shows a data stream wherein somesystem noise can be detected, however signal artifacts 142 can beparticularly seen in a portion thereof (and can be detected by methodssuch as described above). The IIR-estimated signal 144, which isillustrated as a solid line, represents the data stream after signalestimation using an IIR filter, such as described above, and appears atleast somewhat “smoothed” on the graph. It is noted that theIIR-estimated signal induces a time lag on the data stream; however itappears to be a particularly good estimate of glucose data values duringsignal artifacts 142, as compared to the FIR filter (FIG. 13), forexample.

To optimize the estimation of the glucose data values, the parameters ofthe IIR filter can be optimized, for example by increasing or decreasingthe cycle lengths (e.g., 10 minutes, 20 minute, 40 minutes, 60 minutes)that are used in the algorithm. Although an increased cycle length canincrease the time lag induced by the IIR filter, an increased cyclelength can also better estimate glucose data values during severe signalartifacts. In other words, the IIR filter, because of its inherentproperties, can be particularly suited for noise of a certain amplitudeand/or characteristic. In one exemplary embodiment, the IIR filter canbe continually applied, however the parameters such as described abovecan be selectively altered based on the severity of the signalartifacts; in another exemplary embodiment, the IIR filter can beapplied only after positive detection of signal artifacts. Selectiveapplication of the IIR filter based on the severity of the signalartifacts is described in more detail below with reference to FIGS. 15to 17.

It is noted that a comparison of linear regression, an FIR filter, andan IIR filter can be advantageous for optimizing their usage in thepreferred embodiments. That is, an understanding the designconsiderations for each algorithm can lead to optimized selection andimplementation of the algorithm, as described in the section entitled,“Selective Application of Signal Replacement Algorithms” herein. Duringsystem noise, as defined herein, all of the above algorithms can besuccessfully implemented during system noise with relative ease. Duringsignal artifacts, however, computational efficiency is greater with anIIR-filter as compared with linear regression and FIR-filter.Additionally, although the time lag associated with an IIR filter can besubstantially greater than that of the linear regression or FIR-filter,this can be advantageous during severe signal artifacts in order toassign greater weight toward the previous, less noisy data in signalestimation.

In another embodiment of Signal Artifacts Replacement, the processorruns a maximum-average (max-average) filtering algorithm. Themax-average algorithm smoothes data based on the discovery that thesubstantial majority of signal artifacts observed after implantation ofglucose sensors in humans, for example, is not distributed evenly aboveand below the actual blood glucose levels. It has been observed thatmany data sets are actually characterized by extended periods in whichthe noise appears to trend downwardly from maximum values withoccasional high spikes such as described in more detail above withreference to FIG. 7B, section 74 b, which is likely in response tolimitations in the system that do not allow the glucose to fully reactat the enzyme layer and/or proper reduction of H₂O₂ at the counterelectrode, for example. To overcome these downward trending signalartifacts, the max-average calculation tracks with the highest sensorvalues, and discards the bulk of the lower values. Additionally, themax-average method is designed to reduce the contamination of the datawith non-physiologically high data from the high spikes.

The max-average calculation smoothes data at a sampling interval (e.g.,every 30 seconds) for transmission to the receiver at a less frequenttransmission interval (e.g., every 5 minutes) to minimize the effects oflow non-physiological data. First, the processor finds and stores amaximum sensor counts value in a first set of sampled data points (e.g.,5 consecutive, accepted, thirty-second data points). A frame shift timewindow finds a maximum sensor counts value for each set of sampled data(e.g., each 5-point cycle length) and stores each maximum value. Theprocessor then computes a rolling average (e.g., 5-point average) ofthese maxima for each sampling interval (e.g., every 30 seconds) andstores these data. Periodically (e.g., every 10^(th) interval), thesensor outputs to the receiver the current maximum of the rollingaverage (e.g., over the last 10 thirty-second intervals as a smoothedvalue for that time period (e.g., 5 minutes)). In one exampleimplementation, a 10-point window can be used, and at the 10^(th)interval, the processor computes the average of the most recent 5 or 10average maxima as the smoothed value for a 5 minute time period.

In some embodiments of the max-average algorithm, an acceptance filtercan also be applied to new sensor data to minimize effects of highnon-physiological data. In the acceptance filter, each sampled datapoint (e.g., every 30 seconds) is tested for acceptance into the maximumaverage calculation. Each new point is compared against the mostrepresentative estimate of the sensor curve at the previous samplinginterface (e.g., 30-second time point), or at a projection to a currentestimated value. To reject high data, the current data point is comparedto the most recent value of the average maximum values over a timeperiod (e.g., 5 sampled data points over a 2.5 minute period). If theratio of current value to the comparison value is greater than a certainthreshold (e.g., about 1.02), then the current data point is replacedwith a previously accepted value (e.g., 30-second value). If the ratioof current value to the comparison value is in at or within a certainrange (e.g., about 1.02 to 0.90), then the current data point isaccepted. If the ratio of current value to the comparison value is lessthan a certain threshold (e.g., about 0.90), then the current data pointis replaced with a previously accepted value. The acceptance filter stepand max-average calculation are continuously run throughout the data set(e.g., fixed 5-minute windows) on a rolling window basis (e.g., every 30seconds).

In some implementations of the acceptance filter, the comparison valuefor acceptance could also be the most recent maximum of 5 acceptedsensor points (more sensitive) or the most recent average over 10averages of 5 maximum values (least sensitive), for example. In someexemplary implementations of the acceptance filter, the projected valuefor the current time point can be based on regression of the last 4accepted 30-second values and/or the last 2 to 4 (5 to 15 min) of the5-minute smoothed values, for example. In some exemplary implementationsof the acceptance filter, the percentage comparisons of +2% and −10% ofcounts value would be replaced by percentage comparisons based on themost recent 24 hour range of counts values; this method would provideimproved sensor specificity as compared to a method based on totalcounts.

In another embodiment of Signal Artifacts Replacement, the processorruns a “Cone of Possibility Replacement Method.” It is noted that thismethod can be performed in the sensor and/or in the receiver. The Coneof Possibility Detection Method utilizes physiological information alongwith glucose signal values in order define a “cone” of physiologicallyfeasible glucose signal values within a human. Particularly,physiological information depends upon the physiological parametersobtained from continuous studies in the literature as well as our ownobservations. A first physiological parameter uses a maximal sustainedrate of change of glucose in humans (e.g., about 4 to 5 mg/dl/min) and amaximum sustained acceleration of that rate of change (e.g., about 0.1to 0.2 mg/min/min). A second physiological parameter uses the knowledgethat rate of change of glucose is lowest at the maxima and minima, whichare the area of greatest risk in patient treatment, such as describedwith reference to Cone of Possibility Detection, above. A thirdphysiological parameter uses the fact that the best solution for theshape of the curve at any point along the curve over a certain timeperiod (e.g., about 20-25 minutes) is a straight line. It is noted thatthe maximum rate of change can be narrowed in some instances. Therefore,additional physiological data can be used to modify the limits imposedupon the Cone of Possibility Replacement Method for sensor glucosevalues. For example, the maximum per minute rate change can be lowerwhen the subject is lying down or sleeping; on the other hand, themaximum per minute rate change can be higher when the subject isexercising, for example.

The Cone of Possibility Replacement Method utilizes physiologicalinformation along with blood glucose data in order to improve theestimation of blood glucose values within a human in an embodiment ofSignal Artifacts Replacement. The Cone of Possibility Replacement Methodcan be performed on raw data in the sensor, on raw data in the receiver,or on smoothed data (e.g., data that has been replaced/estimated in thesensor or receiver by one of the methods described above) in thereceiver.

In a first implementation of the Cone of Possibility Replacement Method,a centerline of the cone can be projected from a number of previous,optionally smoothed, incremental data points (e.g., previous four,5-minute data points). Each predicted cone centerline point (e.g., 5minute point) increases by the slope (S) (e.g., for the regression incounts/minute) multiplied by the data point increment (e.g., 5 minutes).Counts/mg/dL is estimated from glucose and sensor range calculation overthe data set.

In this first implementation of the Cone of Possibility ReplacementMethod, positive and negative cone limits are simple linear functions.Periodically (e.g., every 5 minutes), each sensor data point (optionallysmoothed) is compared to the cone limits projected from the last fourpoints. If the sensor value observed is within the cone limits, thesensor value is retained and used to generate the cone for the next datapoint increment (e.g., 5-minute point). If the sensor value observedfalls outside the high or low cone limit, the value is replaced by thecone limit value, and that value is used to project the next data pointincrement (e.g., 5 minute point, high point, or low point). For example,if the difference between two adjacent 5-minute points exceeds 20 mg/dL,then cone limits are capped at 20 mg/dL increments per 5 minutes, withthe positive limit of the cone being generated by the addition of0.5*A*t² to mid cone value, where A is 0.1 mg/dL/min/min and t is 5minute increments (A is converted to counts/min/min for thecalculation), and the negative limit of the cone being generated by theaddition of −0.5*A*t² to mid cone value. This implementation provides ahigh degree of accuracy and is minimally sensitive to non-physiologicalrapid changes.

The following Table 1 illustrates one example implementation of the Coneof Possibility Replacement Method, wherein the maximum sustained valueobserved for S is about +/−4 mg/dL/min and the maximum value observedfor A is about +/−0.1 mg/dL/min²:

TABLE 1 Mid line Time (mg/dL) Positive Cone Limit Negative Cone Limit 0100 100 100 5 100 + 5 * S 100 + 5 * S + 12.5 * A 100 + 5 * S − 12.5 A 10100 + 10 * S 100 + 10 * S + 50 * A 100 + 10 * S − 50 * A 15 100 + 15 * S100 + 15 * S + 112.5 * A 100 + 15 * S − 112.5 * A 20 100 + 20 * S 100 +20 * S + 200 * A 100 + 20 * S − 200 * A 25 100 + 25 * S 100 + 25 * S +312.5 * A 100 + 25 * S − 312.5 * A

The cone widens for each 5-minute increment for which a sensor valuefails to fall inside the cone up to 30 minutes, such as can be seen inthe table above. At 30 minutes, a cone has likely widened enough tocapture an observed sensor value, which is used, and the cone collapsesback to a 5-minute increment width. If no sensor values are capturedwithin 30 minutes, the cone generation routine starts over using thenext four observed points. In some implementations special rules can beapplied, for example in a case where the change in counts in one5-minute interval exceeds an estimated 30-mg/dL amount. In this case,the next acceptable point can be more than 20 to 30 minutes later. It isnoted that an implementation of this algorithm includes utilizing thecone of possibility to predict glucose levels and alert patients topresent or upcoming dangerous blood glucose levels.

In another alternative embodiment of cone widening, the cone can widenin set multiples (e.g., 20 mg/dL) of equivalent amounts for eachadditional time interval (e.g., 5 minutes), which rapidly widens thecone to accept data.

It is noted that the numerical parameters represent only one exampleimplementation of the Cone of Possibility Replacement Method. Theconcepts can be applied to any numerical parameters as desired forvarious glucose sensor applications.

In another implementation of the Cone of Possibility Replacement Method,sensor calibration data is optimized using the Clarke Error Grid, theConsensus Grid, or an alternative error assessment that assigns risklevels based on the accuracy of matched data pairs. In an example usingthe Clarke Error Grid, because the 10 regions of the Clarke Error Gridare not all symmetric around the Y=X perfect regression, fits to thegrid can be improved by using a multi-line regression to the data.

Accordingly the pivot point method for the counts vs. glucose regressionfit can be used to optimize sensor calibration data to the Clarke ErrorGrid, Consensus Grid, or other clinical acceptability standard. First,the glucose range is divided according to meter values (e.g., at 200mg/dL). Two linear fitting lines are used, which cross at the pivotpoint. The coordinates of the pivot point in counts and glucose value,plus the slope and intercept of the two lines are variable parameters.Some of pivot point coordinates (e.g., 4 out of 6) and slope orintercept of each line can be reset with each iteration, while thechosen coordinates define the remainder. The data are then re-plotted onthe Clarke Error Grid, and changes in point placement and percentages ineach region of the grid are evaluated. To optimize the fit of a data setto a Clark Error Grid, the regression of counts vs. reference glucosecan be adjusted such that the maximum number of points are in the A+Bzones without reducing the A+B percentage, and the number of points areoptimized such that the highest percentage are in the A zone and lowestpercentage are in the D, E and C zones. In general, the points should bedistributed as evenly as possible around the Y=X line. In someembodiments, three distinct lines optimized for clinical acceptabilitycan represent the regression line. In some embodiments, an additionaluseful criterion can be used to compute the root mean squared percentagebias for the data set. Better fits are characterized by reduction in thetotal root mean squared percentage bias. In an alternativeimplementation of the pivot point methods, a predetermined pivot (e.g.,10 degree) of the regression line can be applied when the estimatedblood is above or below a set threshold (e.g., 150 mg/dL), wherein thepivot and threshold are determined from a retrospective analysis of theperformance of a conversion function and its performance at a range ofglucose concentrations.

In another embodiment of Signal Artifacts Replacement, reference changesin electrode potential can be used to estimate glucose sensor dataduring positive detection of signal artifacts from an electrochemicalglucose sensor, the method hereinafter referred to as reference driftreplacement. In this embodiment, the electrochemical glucose sensorcomprises working, counter, and reference electrodes, such as describedwith reference to FIGS. 1, 2 and 10 above. This method exploits thefunction of the reference electrode as it drifts to compensate forcounter electrode limitations during oxygen deficits, pH changes, and/ortemperature changes such as described in more detail above withreference to FIGS. 10A, 10B, and 10C.

Such as described with in more detail with reference to FIG. 10A apotentiostat is generally designed so that a regulated potentialdifference between the reference electrode 102 and working electrode 100is maintained as a constant. The potentiostat allows the counterelectrode voltage to float within a certain voltage range (e.g., frombetween close to the +1.2V observed for the working electrode to as lowas battery ground or 0.0V). The counter electrode voltage measurementwill reside within this voltage range dependent on the magnitude andsign of current being measured at the working electrode and theelectroactive species type and concentration available in theelectrolyte adjacent to the counter electrode 104. This species will beelectrochemically recruited (e.g., reduced/accepting electrons) to equalthe current of opposite sign (e.g., oxidized/donating electrons)occurring at the working electrode 100. It has been discovered that thereduction of dissolved oxygen or hydrogen peroxide from oxygen convertedin the enzyme layer are the primary species reacting at the counterelectrode to provide this electronic current balance in this embodiment.If there are inadequate reducible species (e.g., oxygen) available forthe counter electrode, or if other non-glucose reaction rate limitingphenomena occur (e.g., temperature or pH), the counter electrode can bedriven in its electrochemical search for electrons all the way to groundor 0.0V. However, regardless of the voltage in the counter electrode,the working and counter electrode currents must still maintainsubstantially equivalent currents. Therefore, the reference electrode102 will drift upward creating new oxidizing and reducing potentialsthat maintain equal currents at the working and counter electrodes.

Because of the function of the reference electrode 102, including thedrift that occurs during periods of signal artifacts (e.g., ischemia),the reference electrode can be monitored to determine the severity ofthe signal artifacts on the data stream. Particularly, a substantiallydirect relationship between the reference electrode drift and signalartifacts has been discovered. Using the information contained withinthe CV curve (FIGS. 10B and/or 10C), the measured glucose signal(I_(SENSE)) can be automatically scaled accordingly to replace theseundesired transient effects on the data stream. It is noted that thecircuit described with reference to FIG. 10A can be used to determinethe CV curve on a regularly scheduled basis or as needed. To this end,the desired reference voltage and applied potential are made variable,and the reference voltage can be changed at a defined rate whilemeasuring the signal strength from the working electrode, which allowsfor generation of a CV curve while a sensor is in vivo.

In alternative implementations of the reference drift replacementmethod, a variety of algorithms can therefore be implemented thatreplaces the signal artifacts based on the changes measured in thereference electrode. Linear algorithms, and the like, are suitable forinterpreting the direct relationship between reference electrode driftand the non-glucose rate limiting signal noise such that appropriateconversion to signal noise compensation can be derived.

In other embodiments of Signal Artifacts Replacement, predictionalgorithms, also referred to as projection algorithms, can be used toreplace glucose data signals for data which does not exist because 1) ithas been discarded, 2) it is missing due to signal transmission errorsand the like, or 3) it represents a time period (e.g., future) for whicha data stream has not yet been obtained based on historic and/or presentdata. Prediction/projection algorithms include any of the abovedescribed Signal Artifacts Replacement algorithms, and differ only inthe fact that they are implemented to replace time points/periods duringwhich no data is available (e.g., for the above-described reasons),rather than including that existing data, within the algorithmiccomputation.

In some embodiments, signal replacement/estimation algorithms are usedto predict where the glucose signal should be, and if the actual datastream varies beyond a certain threshold of that projected value, thensignal artifacts are detected. In alternative embodiments, other dataprocessing can be applied alone, or in combination with theabove-described methods, to replace data signals during system noiseand/or signal artifacts.

Selective Application of Signal Replacement Algorithms

FIG. 15 is a flow chart that illustrates a process of selectivelyapplying signal estimation in embodiments.

At block 152, a sensor data receiving module, also referred to as thesensor data module, receives sensor data (e.g., a data stream),including one or more time-spaced sensor data points, such as describedin more detail with reference to block 82 in FIG. 8.

At block 154, a signal artifacts detection module, also referred to asthe signal artifacts detector 154, is programmed to detect transientnon-glucose related signal artifacts in the data stream that have ahigher amplitude than system noise, such as described in more detailwith reference to block 84 in FIG. 8. However, the signal artifactsdetector of this embodiment can additionally detect a severity of signalartifacts. In some embodiments, the signal artifacts detector has one ormore predetermined thresholds for the severity of the signal artifacts(e.g., low, medium, and high). In some embodiments, the signal artifactsdetector numerically represents the severity of signal artifacts basedon a calculation for example, which representation can be used to applyto the signal estimation algorithm factors, such as described in moredetail with reference to block 156.

In one exemplary embodiment, the signal artifacts detection moduleevaluates the amplitude and/or frequency of the transient non-glucoserelated signal artifacts, which amplitude and/or frequency can be usedto define the severity in terms of a threshold (e.g., high or low) or anumeric representation (e.g., a value from 1 to 10). In anotherexemplary embodiment, the signal artifacts detection module evaluates aduration of the transient non-glucose related signal artifacts, suchthat as the duration increases, a severity can be defined in terms of athreshold (e.g., short or long) or a numeric representation (e.g., 10,20, 30, 40, 50, or 60 minutes). In another exemplary embodiment, thesignal artifacts detection module evaluates the frequency content from aFourier Transform and defines severity in terms of a threshold (e.g.,above or below 30 cycles per hour) or a numeric representation (e.g., 50cycles per hour). All of the signal artifacts detection methodsdescribed herein can be implemented to include determining a severity ofthe signal artifacts, threshold, and/or numerical representations.

At block 156, the signal artifacts replacement module, also referred toas the signal estimation module, selectively applies one of a pluralityof signal estimation algorithm factors in response to the severity ofsaid signal artifacts.

In one embodiment, signal artifacts replacement is normally turned off,except during detected signal artifacts. In another embodiment, a firstsignal estimation algorithm (e.g., linear regression, FIR filter etc.)is turned on all the time, and a second signal estimation algorithmoptimized for signal artifacts (e.g., IIR filter, Cone of PossibilityReplacement Method, etc.) is turned on only during positive detection ofsignal artifacts.

In another embodiment, the signal replacement module comprisesprogramming to selectively switch on and off a plurality of distinctsignal estimation algorithms based on the severity of the detectedsignal artifacts. For example, the severity of the signal artifacts canbe defined as high and low. In such an example, a first filter (e.g.,trimmed regression, linear regression, FIR, Reference Electrode Method,etc.) can be applied during low signal artifacts and a second filter(e.g., IIR, Cone of Possibility Method, etc.) can be applied during highsignal artifacts. It is noted that all of the above signal replacementalgorithms can be selectively applied in this manner based on theseverity of the detected signal artifacts.

FIG. 16 is a graph that illustrates an embodiment wherein the signalreplacement module comprises programming to selectively switch on andoff a signal artifacts replacement algorithm responsive to detection ofsignal artifacts. The x-axis represents time in minutes; the firsty-axis 160 represents sensor data output in counts. A raw data signal161, which is illustrated as a dotted line, shows a data stream whereinsome system noise can be detected; however signal artifacts 162 can beparticularly seen in a portion thereof. The second y-axis 164 representscounter-electrode voltage in counts; counter electrode voltage data 165is illustrated as a solid line. It is noted that a counter voltage dropto approximately zero in this example, which is one of numerous methodsprovided for detecting signal artifacts, detects signal artifacts 162.Accordingly, when the system detects the signal artifacts 162, anIIR-filter is selectively switched on in order to replace the signalartifact with an IIR-estimated glucose signal 166, which is illustratedas a heavy solid line. The IIR filter is switched off upon detection ofnegligible signal artifacts (e.g., counter electrode voltage increasingfrom about zero in this embodiment).

FIG. 17 is a graph that illustrates an embodiment wherein the signalartifacts replacement module comprises programming to selectively applydifferent signal artifacts replacement algorithms responsive todetection of signal artifacts. The x-axis represents time in minutes;the first y-axis 170 represents sensor data output in counts. A raw datasignal 171, which is illustrated as a dotted line, shows a data streamwherein some system noise can be detected; however signal artifacts 172can be particularly seen in a portion thereof. The second y-axis 174represents counter-electrode voltage in counts; counter electrodevoltage data 175 is illustrated as a solid line. It is noted that acounter voltage drop to approximately zero in this example, which is oneof numerous methods provided for detecting signal artifacts, detectssignal artifacts 172.

In this embodiment, an FIR filter is applied to the data stream duringdetection of negligible or no signal artifacts (e.g., during no noise tosystem noise in the data stream). Accordingly, normal signal noise(e.g., system noise) can be filtered to replace the data stream with anFIR-filtered data signal 176, which is illustrated by a slightly heavysolid line. However, upon positive detection of signal artifacts (e.g.,detected by approximately zero counter electrode voltage in thisembodiment), the FIR filter is switched off and an IIR-filter isswitched on in order to replace the signal artifacts with anIIR-filtered glucose signal 178, which is illustrated as a heavy solidline. The IIR filter is subsequently switched off and the FIR filter isswitched back on upon detection of negligible signal artifacts (e.g.,counter electrode voltage increasing from about zero in thisembodiment).

In another embodiment, the signal replacement module comprisesprogramming to selectively apply different parameters to a single signalartifacts replacement algorithm (e.g., IIR, Cone of PossibilityReplacement Method, etc.). As an example, the parameters of an algorithmcan be switched according to signal artifacts detection; in such anexample, an IIR filter with a 30-minute cycle length can be used duringtimes of no noise or system noise and a 60-minute cycle length can beused during signal artifacts. As another example, the severity of thesignal artifacts can be defined as short and long; in such an example,an IIR filter with a 30-minute cycle length can be used during the shortsignal artifacts and a 60-minute cycle length can be used during longsignal artifacts. As yet another example, the severity of the signalartifacts can be defined by a numerical representation; in such anexample, the numerical representation can be used to calculate theparameters of the signal replacement algorithm (e.g., IIR, Cone ofPossibility Replacement Method, and Reference Drift Method).

FIG. 18 is a flow chart that illustrates dynamic and intelligentestimation algorithm selection process 296 in an alternative embodiment.

At block 298, the dynamic and intelligent estimation algorithm selectionprocess 296 obtains sensor data, which can be raw, smoothed, and/orotherwise processed. In some embodiments, data matching can use datafrom a raw data stream received from an analyte sensor, such asdescribed at block 53. In some embodiments, data matching can usecalibrated data.

At block 300, the dynamic and intelligent estimation algorithm selectionprocess 296 includes selecting one or more algorithms from a pluralityof algorithms that best fits the measured analyte values. In someembodiments, the estimative algorithm can be selected based onphysiological parameters; for example, in an embodiment wherein theanalyte sensor is a glucose sensor, a first order regression can beselected when the rate of change of the glucose concentration is high,indicating correlation with a straight line, while a second orderregression can be selected when the rate of change of the glucoseconcentration is low, indicating correlation with a curved line. In someembodiments, a first order regression can be selected when the referenceglucose data is within a certain threshold (for example, 100 to 200mg/dL), indicating correlation with a straight line, while a secondorder regression can be selected when the reference glucose data isoutside of a certain threshold (for example, 100 to 200 mg/dL),indicating correlation with a curved line because the likelihood of theglucose concentration turning around (for example, having a curvature)is greatest at high and low values.

Generally, algorithms that estimate analyte values from measured analytevalues include any algorithm that fits the measured analyte values to apattern, and/or extrapolates estimated values for another time period(for example, for a future time period or for a time period during whichdata needs to be replaced). In some embodiments, a polynomial regression(for example, first order, second order, third order, etc.) can be usedto fit measured analyte values to a pattern, and then extrapolated. Insome embodiments, autoregressive algorithms (for example, IIR filter)can be used to fit measured analyte values to a pattern, and thenextrapolated. In some embodiments, measured analyte values can befiltered by frequency before projection (for example, by converting theanalyte values with a Fourier transform, filtering out high frequencynoise, and converting the frequency data back to time values by using aninverse Fourier transform); this data can then be projected forward(extrapolated) along lower frequencies. In some embodiments, measuredanalyte values can be represented with a Wavelet transform (for examplefiltering out specific noise depending on wavelet function), and thenextrapolate forward. In some alternative embodiments, computationalintelligence (for example, neural network-based mapping, fuzzy logicbased pattern matching, genetic-algorithms based pattern matching, andthe like) can be used to fit measured analyte values to a pattern,and/or extrapolate forward. In yet other alternative embodiments,time-series forecasting is employed using methods such as moving average(single or double), exponential smoothing (single, double, or triple),time series decomposition, growth curves, Box-Jenkins, and the like. Theplurality of algorithms of the preferred embodiments can utilize any oneor more of the above-described algorithms, or equivalents, in order tointelligently select estimative algorithms and thereby estimate analytevalues.

In some embodiments, estimative algorithms further include parametersthat consider external influences, such as insulin therapy, carbohydrateconsumption, and the like. In one such example, these additionalparameters can be user input via the user interface 47 or transmittedfrom an external device, such as an insulin pump, remote device, orother computer system. By including such external influences inadditional to historical trend data (measured analyte values), analyteconcentration changes can be better anticipated.

At block 302, the selected one or more algorithms are evaluated based onstatistical, clinical, or physiological parameters. In some embodiments,running each algorithm on the data stream tests each of the one or morealgorithms, and the algorithmic result with the best correlation to themeasured analyte values is selected. In some embodiments, thepluralities of algorithms are each compared for best correlation withphysiological parameters (for example, within known or expected rates ofchange, acceleration, concentration, etc). In some embodiments, thepluralities of algorithms are each compared for best fit within aclinical error grid (for example, within “A” region of Clarke ErrorGrid). Although first and second order algorithms are exemplifiedherein, any two or more algorithms such as described in more detailbelow could be programmed and selectively used based on a variety ofconditions, including physiological, clinical, and/or statisticalparameters.

At block 304, the algorithm(s) selected from the evaluation step isemployed to estimate analyte values for a time period. Accordingly,analyte values are more dynamically and intelligently estimated toaccommodate the dynamic nature of physiological data. Additionalprocesses, for example applying physiological boundaries, evaluation ofthe estimation algorithms after employing the algorithms, evaluating avariation of estimated analyte values, measuring and comparing analytevalues, and the like (e.g., such as described in co-pending U.S.Published Patent Application 2005-0203360 to Brauker et al.) can beapplied to the dynamic and intelligent estimative algorithms describedherein

FIG. 19 is a graph that illustrates dynamic and intelligent estimationalgorithm selection applied to a data stream in one embodiment showingfirst order estimation, second order estimation, and the measuredglucose values for the time period, wherein the second order estimationshows a better correlation to the measured glucose data than the firstorder estimation. The x-axis represents time in minutes. The y-axisrepresents glucose concentration in mg/dL.

In the data of FIG. 19, measured (calibrated) sensor glucose data 306was obtained up to time t=0. At t=0, a first order regression 308 wasperformed on the measured data 306 to estimate the upcoming 15-minutetime period. A second order regression 310 was also performed on thedata to estimate the upcoming 15-minute time period. The intelligentestimation of the preferred embodiments, such as described in moredetail below, chose the second order regression 310 as the preferredalgorithm for estimation based on programmed conditions (at t=0). Thegraph of FIG. 19 further shows the measured glucose values 312 from t=0to t=15 to illustrate that second order regression 310 does in fact moreaccurately correlate with the measured glucose data 312 than first orderregression 308 from t=0 to t=15.

In the example of FIG. 19, the dynamic and intelligent estimationalgorithm selection determined that the second order regression 310 wasthe preferred algorithm for estimation at t=0 based on conditions. Afirst condition was based on a set threshold that considers second orderregression a better fit when measured glucose values are above 200 mg/dLand trending upwardly. A second condition verifies that the curvature ofthe second order regression line appropriately shows a decelerationabove 200 mg/dL. Although two specific examples of conditions aredescribed herein, dynamic and intelligent estimation can have as many oras few conditions programmed therein as can be imagined or contrived.Some additional examples of conditions for selecting from a plurality ofalgorithms are listed above, however the scope of this aspect of dynamicand intelligent estimation includes any conditional statements that canbe programmed and applied to any algorithms that can be implemented forestimation.

FIG. 20 is a flow chart that illustrates the process 330 of dynamic andintelligent estimation and evaluation of analyte values in oneembodiment, wherein the estimation algorithms are continuously,periodically, or intermittently evaluated based on statistical,clinical, or physiological parameters to maintain accuracy ofestimation.

At block 332, the dynamic and intelligent estimation and evaluationprocess 130 obtains sensor data, which can be raw, smoothed, calibratedand/or otherwise processed.

At block 334, the dynamic and intelligent estimation and evaluationprocess 330 estimates one or more analyte values using one or moreestimation algorithms. In some embodiments, this analyte valueestimation uses conventional projection using first or second orderregression, for example. In some embodiments, dynamically andintelligently selecting of one or more algorithms from a plurality ofalgorithms, dynamically and intelligently estimating analyte valueswithin physiological boundaries, evaluating a variation of estimatedanalyte values, measuring and comparing analyte values, and the like(e.g., such as described in U.S. Publication No. US-2005-0203360-A1) canbe applied to the dynamic and intelligent estimation and evaluationprocess described herein.

The estimative algorithms described elsewhere herein considermathematical equations, for example, which may or may not be sufficientto accurately estimate analyte values in some circumstances due to thedynamic nature of mammalian behavior. For example, in a circumstancewhere a patient's glucose concentration is trending upwardly at aconstant rate of change (for example, 120 mg/dL at 2 mg/dL/min), anexpected physiological pattern would likely estimate a continuedincrease at substantially the same rate of change over the upcomingapproximately 40 minutes, which would fall within physiologicalboundaries. However, if a person with diabetes were to engage in heavyaerobic exercise, which may not be known by the estimative algorithm, aslowing of the upward trend, and possibly a change to a downward trendcan possibly result, leading to inaccuracies in the estimated analytevalues. Numerous such circumstances can occur in the lifestyle of aperson with diabetes. However, although analyte values can sometimes beestimated under “normal” circumstances, other circumstances exist thatare not “normal” or “expected” and can result in estimative algorithmsthat produce apparently erroneous results, for example, if they arebased solely on mathematical calculations and/or physiological patterns.Accordingly, evaluation of the estimative algorithms can be performed toensure the accuracy or quantify a measure of confidence in theestimative algorithms.

At block 336, the dynamic and intelligent estimation and evaluationprocess 330 evaluates the estimation algorithms employed at block 334 toevaluate a “goodness” of the estimated analyte values. The evaluationprocess performs an evaluation of the measured analyte data with thecorresponding estimated analyte data (e.g., by performing the algorithmon the data stream and comparing the measured with the correspondinganalyte data for a time period). In some embodiments, evaluation can beperformed continually or continuously so that the dynamic andintelligent algorithms are continuously adapting to the changingphysiological analyte data. In some embodiments, the evaluation can beperformed periodically so that the dynamic and intelligent algorithmsare periodically and systematically adapting to the changingphysiological analyte data. In some embodiments, evaluation can beperformed intermittently, for example when an estimative algorithm isinitiated or when other such triggers occur, so that the dynamic andintelligent algorithms can be evaluated when new or updated data oralgorithms are being processed.

This evaluation process 330 uses any known evaluation method, forexample based on statistical, clinical, or physiological standards. Oneexample of statistical evaluation is provided below with reference toFIG. 21; however other methods are also possible. In some embodiments,the evaluation process 330 determines a correlation coefficient ofregression. In some embodiments wherein the sensor is a glucose sensor,the evaluation process 330 determines if the selected estimativealgorithm shows that analyte values fall with the “A” and “B” regions ofthe Clarke Error Grid. Other parameters or methods for evaluation areconsidered within the scope of the preferred embodiments. In someembodiments, the evaluation process 330 includes performing a curvatureformula to determine fiducial information about the curvature, whichresults in an evaluation of the amount of noise on the signal.

In some embodiments, the evaluation process 330 calculates physiologicalboundaries to evaluate whether the estimated analyte values fall withinknown physiological constraints. In this embodiment, the estimativealgorithm(s) are evaluated to ensure that they do not allow estimatedanalyte values to fall outside of physiological boundaries, someexamples of which are described in more detail elsewhere herein, and inco-pending U.S. Published Patent Application 2005-0203360 to Brauker etal., for example. In some alternative embodiments, clinical orstatistical parameters can be used in a similar manner to boundestimated analyte values.

If the result of the evaluation is satisfactory (for example, 10%average deviation, correlation coefficient above 0.79, all estimatedanalyte values within A or B region of the Clarke Error Grid, allestimated analyte values within physiological boundaries, and the like),the processing continues to the next step, using the selected estimativealgorithm. However, if the result of the evaluation is unsatisfactory,the process can start the algorithm selection process again, optionallyconsidering additional information, or the processor can determine thatestimation is not appropriate for a certain time period. In onealternative embodiment, a signal noise measurement can be evaluated, andif the signal to noise ratio is unacceptable, the processor can modifyits estimative algorithm or other action that can help compensate forsignal noise (e.g., signal artifacts, such as described elsewhereherein).

FIG. 21 is a graph that illustrates an evaluation of the selectedestimative algorithm in one embodiment, wherein a correlation ismeasured to determine a deviation of the measured glucose data with theselected estimative algorithm, if any. The x-axis represents time inminutes. The y-axis represents glucose concentration in mg/dL. Measuredglucose values 340 are shown for about 90 minutes up to t=0. At t=0, theselected algorithm is performed on 40 minutes of the measured glucosevalues 340 up to t=0, which is represented by a regression line 342 inthis embodiment. A data association function is used to determine agoodness of fit of the estimative algorithm on the measured glucose data340; namely, the estimative algorithm is performed retrospectively onthe measured glucose data 340, and is hereinafter referred to asretrospectively estimated glucose data 342 (e.g., estimation prior tot=0), after which a correlation (or deviation) with the measured glucosedata is determined. In this example, the goodness of fit shows a meanabsolute relative difference (MARD) of 3.3% between the measured glucosedata 340 and the retrospectively estimated glucose data 342. While notwishing to be bound to theory, it is believed that this correlation ofthe measured glucose data 340 to the retrospectively estimated glucosedata 342 can be indicative of the correlation of future estimatedglucose data to the measured glucose data for that estimated timeperiod.

Reference is now made to FIG. 22, which is a flow chart that illustratesthe process 450 of analyzing a variation of estimated future analytevalue possibilities in one embodiment. This embodiment takes intoconsideration that analyte values are subject to a variety of externalinfluences, which can cause the measured analyte values to alter fromthe estimated analyte values as the time period that was estimatedpasses. External influences include, but are not limited to, exercise,sickness, consumption of food and alcohol, injections of insulin, othermedications, and the like. For a person with diabetes, for example, evenwhen estimation does not accurately predict the upcoming measuredanalyte values, the estimated analyte values can be valuable to apatient in treatment and in fact can even alter the estimated path byencouraging proactive patient behavior that can cause the patient toavoid the estimated clinical risk. In other words, the deviation ofmeasured analyte values from their corresponding estimated analytevalues may not be an “error” in the estimative algorithm, and is in factone of the benefits of the continuous analyte sensor of the preferredembodiments, namely encouraging patient behavior modification andthereby improving patient health through minimizing clinically riskyanalyte values. Proactive behavior modification (for example, therapiessuch as insulin injections, carbohydrate consumption, exercise, and thelike) can cause the patient's measured glucose to change from theestimated path, and analyzing a variation that can be associated withthe estimated analyte values can encompass many of these changes.Therefore, in addition to estimated analyte values, a variation can becalculated or estimated based on statistical, clinical, and/orphysiological parameters that provides a range of values in which theestimated analyte values can fall.

At block 452, the variation of possible estimated analyte valuesanalysis process 450 obtains sensor data, which can be raw, smoothed,calibrated and/or otherwise processed.

At block 454, the variation of possible estimated analyte valuesanalysis process 450 estimates one or more analyte values using one ormore estimation algorithms. In some embodiments, this analyte valuesestimation uses conventional projection using first or second orderregression, for example. In some embodiments, dynamically andintelligently selecting of one or more algorithms from a plurality ofalgorithms, dynamically and intelligently estimating analyte valueswithin physiological boundaries, dynamic and intelligent estimation andevaluation of estimated analyte values, measuring and comparing analytevalues (e.g., such as described in U.S. Publication No.US-2005-0203360-A1), and the like can be applied to the dynamic andintelligent estimation and evaluation process described herein.

At block 456, the variation of possible estimated analyte valuesevaluation process 450 analyzes a variation of the estimated analytedata. Particularly, a statistical, clinical, and/or physiologicalvariation of estimated analyte values can be calculated when applyingthe estimative algorithms and/or can be calculated at regular intervalsto dynamically change as the measured analyte values are obtained. Ingeneral, analysis of trends and their variation allows the estimation ofthe preferred embodiments to dynamically and intelligently anticipateupcoming conditions, by considering internal and external influencesthat can affect analyte concentration.

In some embodiments, physiological boundaries for analytes in mammalscan be used to set the boundaries of variation. For example, knownphysiological boundaries of glucose in humans are discussed in detailwith reference to U.S. Publication No. US-2005-0203360-A1, however anyphysiological parameters for any measured analyte can be implementedhere to provide this variation of physiologically feasible analytevalues.

In some embodiments, statistical variation can be used to determine avariation of possible analyte values. Statistical variation can be anyknown divergence or change from a point, line, or set of data based onstatistical information. Statistical information includes patterns ordata analysis resulting from experiments, published or unpublished, forexample. In some embodiments, statistical information can include normalpatterns that have been measured statistically in studies of analyteconcentrations in mammals, for example. In some embodiments, statisticalinformation can include errors observed and measured statistically instudies of analyte concentrations in mammals, for example. In someembodiments, statistical information can include predeterminedstatistical standards, for example, deviation less than or equal to 5%on the analyte value. In some embodiments, statistical variation can bea measured or otherwise known signal noise level.

In some embodiments, a variation is determined based on the fact thatthe conventional blood glucose meters are known to have up to a +/−20%error in glucose values (namely, on average in the hands of a patient).For example, gross errors in glucose readings are known to occur due topatient error in self-administration of the blood glucose test. In onesuch example, if the user has traces of sugar on his/her finger whileobtaining a blood sample for a glucose concentration test, then themeasured glucose value will likely be much higher than the measuredglucose value in the blood. Additionally, it is known thatself-monitored blood glucose tests (for example, test strips) areoccasionally subject to manufacturing error. In view of this statisticalinformation, in an embodiment wherein a continuous glucose sensor reliesupon a conventional blood glucose meter for calibration, this +/−20%error should be considered because of the potential for translatedeffect on the calibrated sensor analyte data. Accordingly, thisexemplary embodiment would provide for a +/−20% variation of estimatedglucose values based on the above-described statistical information.

In some embodiments, a variation of estimated analyte values can beanalyzed based on individual physiological patterns. Physiologicalpatterns are affected by a combination of at least biologicalmechanisms, physiological boundaries, and external influences such asexercise, sickness, consumption of food and alcohol, injections ofinsulin, other medications, and the like. Advantageously, patternrecognition can be used with continuous analyte sensors to characterizean individual's physiology; for example the metabolism of a person withdiabetes can be individually characterized, which has been difficult toquantify with conventional glucose sensing mechanisms due to the uniquenature of an individual's metabolism. Additionally, this information canbe advantageously linked with external influences (for example, patientbehavior) to better understand the nature of individual humanphysiology, which can be helpful in controlling the basal rate in aperson with diabetes, for example.

While not wishing to be bound to theory, it is believed that monitoringof individual historical physiological analyte data can be used torecognize patterns that can be used to estimate analyte values, orranges of values, in a mammal. For example, measured analyte data for apatient can show certain peaks of glucose levels during a specific timeof day, “normal” AM and PM eating behaviors (for example, that follow apattern), weekday versus weekend glucose patterns, individual maximumrate of change, and the like, that can be quantified usingpatient-dependent pattern recognition algorithms, for example. Patternrecognition algorithms that can be used in this embodiment include, butare not limited to, stochastic nonlinear time-series analysis,exponential (non-linear) autoregressive model, process feedbacknonlinear autoregressive (PFNAR) model, neural networks, and the like.

Accordingly, statistically calculated patterns can provide informationuseful in analyzing a variation of estimated analyte values for apatient that includes consideration of the patient's normalphysiological patterns. Pattern recognition enables the algorithmicanalysis of analyte data to be customized to a user, which is usefulwhen analyte information is variable with each individual user, such ashas been seen in glucose in humans, for example.

In some embodiments, a variation of estimated analyte values is onclinical risk analysis. Estimated analyte values can have higherclinical risk in certain ranges of analyte values, for example analytevalues that are in a clinically risky zone or analyte values that arechanging at a clinically risky rate of change. When a measured analytevalue or an estimated analyte value shows existing or approachingclinical risk, it can be important to analyze the variation of estimatedanalyte values in view of the clinical risk to the patient. For example,in an effort to aid a person with diabetes in avoiding clinically riskyhyper- or hypoglycemia, a variation can be weighted toward theclinically risk zone, which can be used to emphasize the pending dangerto the patient, doctor, or care taker, for example. As another example,the variation of measured or estimated analyte values can be based onvalues that fall within the “A” and/or “B” regions of an error gridAnalysis Method.

In case of variation analysis based on clinical risk, the estimatedanalyte values are weighted in view of pending clinical risk. Forexample, if estimated glucose values show a trend toward hypoglycemia ata certain rate of change, a variation of possible trends towardhypoglycemia are weighted to show how quickly the glucose concentrationcould reach 40 mg/dL, for example. As another example, if estimatedglucose values show a trend toward hyperglycemia at a certainacceleration, a variation of possible trends toward hyperglycemia areweighted to show how quickly the glucose concentration could reach 200mg/dL, for example.

In some embodiments, when a variation of the estimated analyte valuesshows higher clinical risk as a possible path within that variationanalysis as compared to the estimated analyte path, the estimatedanalyte values can be adjusted to show the analyte values with the mostclinical risk to a patient. While not wishing to be bound by theory,adjusting the estimated analyte values for the highest variation ofclinical risk exploits the belief that by showing the patient the “worstcase scenario,” the patient is more likely to address the clinical riskand make timely behavioral and therapeutic modifications and/ordecisions that will slow or reverse the approaching clinical risk.

At block 458, the variation of possible estimated analyte valuesevaluation process 150 provides output based on the variation analysis.In some embodiments, the result of this variation analysis provides a“zone” of possible values, which can be displayed to the user,considered in data analysis, and/or used in evaluating of performance ofthe estimation, for example.

FIG. 23 is a graph that illustrates variation analysis of estimatedglucose values in one embodiment, wherein a variation of the estimatedglucose values is analyzed and determined based on known physiologicalparameters. The x-axis represents time in minutes. The y-axis representsglucose concentration in mg/dL. In this embodiment, the known maximumrate of change and acceleration of glucose in humans are used to providethe variation about the estimated glucose path.

The measured glucose values 460 are shown for about 90 minutes up tot=0. At t=0, intelligent and dynamic estimation of the preferredembodiments is performed to obtain estimated glucose values 462. Avariation of estimated glucose values is then determined based onphysiological parameters, including an upper limit 464 and a lower limit466 of variation defined by known physiological parameters, includingrate of change and acceleration of glucose concentration in humans.

FIG. 24 is a graph that illustrates variation of estimated analytevalues in another embodiment, wherein the variation is based onstatistical parameters. The x-axis represents time in minutes and they-axis represents glucose concentration in mg/dL. The measured glucosevalues 470 are shown for about 160 minutes up to t=0. At t=0,intelligent and dynamic estimation of the preferred embodiments isemployed to obtain estimated glucose values 472. A variation is definedby upper and lower limits 474 that were determined using 95% confidenceintervals. Bremer, T.; Gough, D. A. “Is blood glucose predictable fromprevious values? A solicitation for data.” Diabetes 1999, 48, 445-451,which is incorporated by reference herein in its entirety, teaches amethod of determining a confidence interval in one embodiment.

Although some embodiments have been described for a glucose sensor, anymeasured analyte pattern, data analysis resulting from an experiment, orotherwise known statistical information, whether official or unofficial,published or unpublished, proven or anecdotal, and the like, can be usedto provide the statistical variation described herein.

FIG. 25 is a flow chart that illustrates the process 480 of estimating,measuring, and comparing analyte values in one embodiment.

At block 482, the estimating, measuring, and comparing analyte valuesprocess 480 obtains sensor data, which can be raw, smoothed, calibratedand/or otherwise processed.

At block 484, the estimating, measuring, and comparing analyte valuesprocess 480 estimates one or more analyte values for a time period. Insome embodiments, this analyte values estimation uses conventionalprojection using first or second order regression, for example. In someembodiments, dynamically and intelligently selecting of one or morealgorithms from a plurality of algorithms, dynamically and intelligentlyestimating analyte values within physiological boundaries), dynamic andintelligent estimation and evaluation of estimated analyte values,variation analysis (e.g., such as described in co-pending U.S. PublishedPatent Application 2005-0203360 to Brauker et al.), and the like can beapplied to the process described herein.

At block 486, the estimating, measuring, and comparing analyte valuesprocess 480 obtains sensor data for the time period for which theestimated analyte values were calculated at block 484. In someembodiments, the measured analyte data can be raw, smoothed, calibrated,and/or otherwise processed.

At block 488, the estimating, measuring, and comparing analyte valuesprocess 480 compares the estimated analyte data to the measured analytedata for that estimated time period. In general, it can be useful tocompare the estimated analyte data to the measured analyte data for thatestimated time period after estimation of analyte values. Thiscomparison can be performed continuously, namely, at regular intervalsas data streams are processed into measured analyte values.Alternatively, this comparison can be performed based on events, such asduring estimation of measured analyte values, selection of a estimativealgorithm, evaluation of estimative algorithms, variation analysis ofestimated analyte values, calibration and transformation of sensoranalyte data, and the like.

One embodiment is shown in FIG. 26, wherein MARD is used to determine acorrelation (or deviation), if any, between the estimated and measureddata sets. In other embodiments, other methods, such as linearregression, non-linear mapping/regression, rank (for example,non-parametric) correlation, least mean square fit, mean absolutedeviation (MAD), and the like, can be used to compare the estimatedanalyte data to the measured analyte data to determine a correlation (ordeviation), if any.

In one embodiment, wherein estimation is used in outlier detectionand/or in matching data pairs for a continuous glucose sensor (see FIGS.5 and 6), the estimated glucose data can be plotted against referenceglucose data on a clinical error grid (for example, Clarke Error Grid orrate grid) and then compared to the measured glucose data for thatestimated time period plotted against the same reference analyte data onthe same clinical error grid. In alternative embodiments, other clinicalerror analysis methods can be used, such as Consensus Error Grid, rateof change calculation, consensus grid, and standard clinical acceptancetests, for example. The deviation can be quantified by percentdeviation, or can be classified as pass/fail, for example.

In some embodiments, the results of the comparison provide aquantitative deviation value, which can be used to provide a statisticalvariation; for example, if the % deviation is calculated as 8%, then thestatistical variation such as described with reference to FIG. 22 can beupdated with a +/−8% variation. In some alternative embodiments, theresults of the comparison can be used to turn on/off the estimativealgorithms, estimative output, and the like. In general, the comparisonproduces a confidence interval (for example, +/−8% of estimated values)which can be used in data analysis, output of data to a user, and thelike.

A resulting deviation from this comparison between estimated andcorresponding measured analyte values may or may not imply error in theestimative algorithms. While not wishing to be bound by theory, it isbelieved that the deviation between estimated and corresponding measuredanalyte values is due, at least in part, to behavioral changes by apatient, who observes estimated analyte values and determines to changethe present trend of analyte values by behavioral and/or therapeuticchanges (for example, medication, carbohydrate consumption, exercise,rest, and the like). Accordingly, the deviation can also be used toillustrate positive changes resulting from the educational aspect ofproviding estimated analyte values to the user, for example.

FIG. 26 is a graph that illustrates comparison of estimated analytevalues in one embodiment, wherein previously estimated analyte valuesare compared to time corresponding measured analyte values to determinea correlation (or deviation), if any. The x-axis represents time inminutes. The y-axis represents glucose concentration in mg/dL. Themeasured glucose values 492 are shown for about 105 minutes up to t=15.The estimated analyte values 494, which were estimated at t=0 for 15minutes, are shown superimposed over the measured analyte values 492.Using a 3-point MARD for t=0 to t=15, the estimated analyte values 494can be compared with the measured analyte values 492 to determine a0.55% average deviation.

FIG. 27 provides a flow chart 520 that illustrates the evaluation ofreference and/or sensor data for statistical, clinical, and/orphysiological acceptability in one embodiment. Although someacceptability tests are disclosed herein, any known statistical,clinical, physiological standards and methodologies can be applied toevaluate the acceptability of reference and sensor analyte data.

One cause for discrepancies in reference and sensor data is asensitivity drift that can occur over time, when a sensor is insertedinto a host and cellular invasion of the sensor begins to blocktransport of the analyte to the sensor, for example. Therefore, it canbe advantageous to validate the acceptability of converted sensor dataagainst reference analyte data, to determine if a drift of sensitivityhas occurred and whether the calibration should be updated.

In one embodiment, the reference analyte data is evaluated with respectto substantially time corresponding converted sensor data to determinethe acceptability of the matched pair. For example, clinicalacceptability considers a deviation between time corresponding analytemeasurements (for example, data from a glucose sensor and data from areference glucose monitor) and the risk (for example, to the decisionmaking of a person with diabetes) associated with that deviation basedon the glucose value indicated by the sensor and/or reference data.Evaluating the clinical acceptability of reference and sensor analytedata, and controlling the user interface dependent thereon, can minimizeclinical risk. Preferably, the receiver evaluates clinical acceptabilityeach time reference data is obtained.

After initial calibration, such as is described in more detail withreference to FIG. 5, the sensor data receiving module receivessubstantially continuous sensor data (e.g., a data stream) via areceiver and converts that data into estimated analyte values. As usedherein, the term “substantially continuous” is a broad term and is usedin its ordinary sense, without limitation, to refer to a data stream ofindividual measurements taken at time intervals (e.g., time-spaced)ranging from fractions of a second up to, e.g., 1, 2, or 5 minutes ormore. As sensor data is continuously converted, it can be occasionallyrecalibrated in response to changes in sensor sensitivity (drift), forexample. Initial calibration and re-calibration of the sensor require areference analyte value. Accordingly, the receiver can receive referenceanalyte data at any time for appropriate processing.

At block 522, the reference data receiving module, also referred to asthe reference input module, receives reference analyte data from areference analyte monitor. In one embodiment, the reference datacomprises one analyte value obtained from a reference monitor. In somealternative embodiments however, the reference data includes a set ofanalyte values entered by a user into the interface and averaged byknown methods, such as are described elsewhere herein. In somealternative embodiments, the reference data comprises a plurality ofanalyte values obtained from another continuous analyte sensor.

The reference data can be pre-screened according to environmental andphysiological issues, such as time of day, oxygen concentration,postural effects, and patient-entered environmental data. In oneexemplary embodiment, wherein the sensor comprises an implantableglucose sensor, an oxygen sensor within the glucose sensor is used todetermine if sufficient oxygen is being provided to successfullycomplete the necessary enzyme and electrochemical reactions for accurateglucose sensing. In another exemplary embodiment, the patient isprompted to enter data into the user interface, such as meal timesand/or amount of exercise, which can be used to determine likelihood ofacceptable reference data. In yet another exemplary embodiment, thereference data is matched with time-corresponding sensor data, which isthen evaluated on a modified clinical error grid to determine itsclinical acceptability.

Some evaluation data, such as described in the paragraph above, can beused to evaluate an optimum time for reference analyte measurement.Correspondingly, the user interface can then prompt the user to providea reference data point for calibration within a given time period.Consequently, because the receiver proactively prompts the user duringoptimum calibration times, the likelihood of error due to environmentaland physiological limitations can decrease and consistency andacceptability of the calibration can increase.

At block 524, the evaluation module, also referred to as acceptabilitymodule, evaluates newly received reference data. In one embodiment, theevaluation module evaluates the clinical acceptability of newly receivedreference data and time corresponding converted sensor data (new matcheddata pair). In one embodiment, a clinical acceptability evaluationmodule 524 matches the reference data with a substantially timecorresponding converted sensor value, and determines the Clarke ErrorGrid coordinates. In this embodiment, matched pairs that fall within theA and B regions of the Clarke Error Grid are considered clinicallyacceptable, while matched pairs that fall within the C, D, and E regionsof the Clarke Error Grid are not considered clinically acceptable.

A variety of other known methods of evaluating clinical acceptabilitycan be utilized. In one alternative embodiment, the Consensus Grid isused to evaluate the clinical acceptability of reference and sensordata. In another alternative embodiment, a mean absolute differencecalculation can be used to evaluate the clinical acceptability of thereference data. In another alternative embodiment, the clinicalacceptability can be evaluated using any relevant clinical acceptabilitytest, such as a known grid (e.g., Clarke Error or Consensus), andadditional parameters, such as time of day and/or the increase ordecreasing trend of the analyte concentration. In another alternativeembodiment, a rate of change calculation can be used to evaluateclinical acceptability. In yet another alternative embodiment, whereinthe received reference data is in substantially real time, theconversion function could be used to predict an estimated glucose valueat a time corresponding to the time stamp of the reference analyte value(this can be required due to a time lag of the sensor data such asdescribed elsewhere herein). Accordingly, a threshold can be set for thepredicted estimated glucose value and the reference analyte valuedisparity, if any. In some alternative embodiments, the reference datais evaluated for physiological and/or statistical acceptability asdescribed in more detail elsewhere herein.

At decision block 526, results of the evaluation are assessed. Ifacceptability is determined, then processing continues to block 528 tore-calculate the conversion function using the new matched data pair inthe calibration set.

At block 528, the conversion function module re-creates the conversionfunction using the new matched data pair associated with the newlyreceived reference data. In one embodiment, the conversion functionmodule adds the newly received reference data (e.g., including thematched sensor data) into the calibration set, and recalculates theconversion function accordingly. In alternative embodiments, theconversion function module displaces the oldest, and/or least concordantmatched data pair from the calibration set, and recalculates theconversion function accordingly.

At block 530, the sensor data transformation module uses the newconversion function (from block 528) to continually (or intermittently)convert sensor data into estimated analyte values, also referred to ascalibrated data, or converted sensor data, such as is described in moredetail above.

At block 532, an output module provides output to the user via the userinterface. The output is representative of the estimated analyte value,which is determined by converting the sensor data into a meaningfulanalyte value. User output can be in the form of a numeric estimatedanalyte value, an indication of directional trend of analyteconcentration, and/or a graphical representation of the estimatedanalyte data over a period of time, for example. Other representationsof the estimated analyte values are also possible, for example audio andtactile.

If, however, acceptability is determined at decision block 526 asnegative (unacceptable), then the processing progresses to block 534 toadjust the calibration set. In one embodiment of a calibration setadjustment, the conversion function module removes one or more oldestmatched data pair(s) and recalculates the conversion functionaccordingly. In an alternative embodiment, the conversion functionmodule removes the least concordant matched data pair from thecalibration set, and recalculates the conversion function accordingly.

At block 536, the conversion function module re-creates the conversionfunction using the adjusted calibration set. While not wishing to bebound by theory, it is believed that removing the least concordantand/or oldest matched data pair(s) from the calibration set can reduceor eliminate the effects of sensor sensitivity drift over time,adjusting the conversion function to better represent the currentsensitivity of the sensor.

At block 524, the evaluation module re-evaluates the acceptability ofnewly received reference data with time corresponding converted sensordata that has been converted using the new conversion function (block536). The flow continues to decision block 538 to assess the results ofthe evaluation, such as described with reference to decision block 526,above. If acceptability is determined, then processing continues toblock 530 to convert sensor data using the new conversion function andcontinuously display calibrated sensor data on the user interface.

If, however, acceptability is determined at decision block 526 asnegative, then the processing loops back to block 534 to adjust thecalibration set once again. This process can continue until thecalibration set is no longer sufficient for calibration, for example,when the calibration set includes only one or no matched data pairs withwhich to create a conversion function. In this situation, the system canreturn to the initial calibration or start-up mode, which is describedin more detail with reference to FIGS. 16 and 19, for example.Alternatively, the process can continue until inappropriate matched datapairs have been sufficiently purged and acceptability is positivelydetermined.

In alternative embodiments, the acceptability is determined by a qualityevaluation, for example, calibration quality can be evaluated bydetermining the statistical association of data that forms thecalibration set, which determines the confidence associated with theconversion function used in calibration and conversion of raw sensordata into estimated analyte values. See, e.g., U.S. Publication No.US-2005-0027463-A1.

Alternatively, each matched data pair can be evaluated based on clinicalor statistical acceptability such as described above; however, when amatched data pair does not pass the evaluation criteria, the system canbe configured to ask for another matched data pair from the user. Inthis way, a secondary check can be used to determine whether the erroris more likely due to the reference glucose value or to the sensorvalue. If the second reference glucose value substantially correlates tothe first reference glucose value, it can be presumed that the referenceglucose value is more accurate and the sensor values are errant. Somereasons for errancy of the sensor values include a shift in the baselineof the signal or noise on the signal due to low oxygen, for example. Insuch cases, the system can be configured to re-initiate calibrationusing the secondary reference glucose value. If, however, the referenceglucose values do not substantially correlate, it can be presumed thatthe sensor glucose values are more accurate and the reference glucosevalues eliminated from the algorithm.

FIG. 28 is a flow chart 550 that illustrates the evaluation ofcalibrated sensor data for aberrant values in one embodiment. Althoughsensor data are typically accurate and reliable, it can be advantageousto perform a self-diagnostic check of the calibrated sensor data priorto displaying the analyte data on the user interface.

One reason for anomalies in calibrated sensor data includes transientevents, such as local ischemia at the implant site, which cantemporarily cause erroneous readings caused by insufficient oxygen toreact with the analyte. Accordingly, the flow chart 550 illustrates oneself-diagnostic check that can be used to catch erroneous data beforedisplaying it to the user.

At block 552, a sensor data receiving module, also referred to as thesensor data module, receives new sensor data from the sensor.

At block 554, the sensor data transformation module continuously (orintermittently) converts new sensor data into estimated analyte values,also referred to as calibrated data.

At block 556, a self-diagnostic module compares the new calibratedsensor data with previous calibrated sensor data, for example, the mostrecent calibrated sensor data value. In comparing the new and previoussensor data, a variety of parameters can be evaluated. In oneembodiment, the rate of change and/or acceleration (or deceleration) ofchange of various analytes, which have known physiological limits withinthe body, and sensor data can be evaluated accordingly. For example, alimit can be set to determine if the new sensor data is within aphysiologically feasible range, indicated by a rate of change from theprevious data that is within known physiological (and/or statistical)limits. Similarly, any algorithm that predicts a future value of ananalyte can be used to predict and then compare an actual value to atime corresponding predicted value to determine if the actual valuefalls within a statistically and/or clinically acceptable range based onthe predictive algorithm, for example. In certain embodiments,identifying a disparity between predicted and measured analyte data canbe used to identify a shift in signal baseline responsive to anevaluated difference between the predicted data and time-correspondingmeasured data. In some alternative embodiments, a shift in signalbaseline and/or sensitivity can be determined by monitoring a change inthe conversion function; namely, when a conversion function isre-calculated using the equation y=mx+b, a change in the values of m(sensitivity) or b (baseline) above a pre-selected “normal” threshold,can be used to trigger a fail-safe or further diagnostic evaluation.

Although the above-described self-diagnostics are generally employedwith calibrated sensor data, some alternative embodiments arecontemplated that check for aberrancy of consecutive sensor values priorto sensor calibration, for example, on the raw data stream and/or afterfiltering of the raw data stream. In certain embodiments, anintermittent or continuous signal-to-noise measurement can be evaluatedto determine aberrancy of sensor data responsive to a signal-to-noiseratio above a set threshold. In certain embodiments, signal residuals(e.g., by comparing raw and filtered data) can be intermittently orcontinuously analyzed for noise above a set threshold. In certainembodiments, pattern recognition can be used to identify noiseassociated with physiological conditions, such as low oxygen or otherknown signal aberrancies. Accordingly, in these embodiments, the systemcan be configured, in response to aberrancies in the data stream, totrigger signal estimation, adaptively filter the data stream accordingto the aberrancy, and the like, as described in more detail herein.

In another embodiment, reference analyte values are processed todetermine a level of confidence, wherein reference analyte values arecompared to their time-corresponding calibrated sensor values andevaluated for clinical or statistical accuracy. In yet anotheralternative embodiment, new and previous reference analyte data arecompared in place of or in addition to sensor data. In general, thereexist known patterns and limitations of analyte values that can be usedto diagnose certain anomalies in raw or calibrated sensor and/orreference analyte data.

At decision block 558, the system determines whether the comparisonreturned aberrant values. In one embodiment, the slope (rate of change)between the new and previous sensor data is evaluated, wherein valuesgreater than +/−10, 15, 20, 25, or 30% or more change and/or +/−2, 3, 4,5, 6 or more mg/dL/min, more preferably +/−4 mg/dL/min, rate of changeare considered aberrant. In certain embodiments, other knownphysiological parameters can be used to determine aberrant values.However, a variety of comparisons and limitations can be set.

At block 560, if the values are not found to be aberrant, the sensordata transformation module continuously (or intermittently) convertsreceived new sensor data into estimated analyte values, also referred toas calibrated data.

At block 562, if the values are found to be aberrant, the system goesinto a suspended mode, also referred to as fail-safe mode in someembodiments, which is described in more detail below with reference toFIG. 29. In general, suspended mode suspends display of calibratedsensor data and/or insertion of matched data pairs into the calibrationset. Preferably, the system remains in suspended mode until receivedsensor data is not found to be aberrant. In certain embodiments, a timelimit or threshold for suspension is set, after which system and/or userinteraction can be required, for example, requesting additionalreference analyte data, replacement of the electronics unit, and/orreset.

In some alternative embodiments, in response to a positive determinationof aberrant value(s), the system can be configured to estimate one ormore glucose values for the time period during which aberrant valuesexist. Signal estimation generally refers to filtering, data smoothing,augmenting, projecting, and/or other methods for estimating glucosevalues based on historical data, for example. In one implementation ofsignal estimation, physiologically feasible values are calculated basedon the most recent glucose data, and the aberrant values are replacedwith the closest physiologically feasible glucose values. See also U.S.Publication No. US-2005-0027463-A1.

FIG. 29 provides a flow chart 580 that illustrates a self-diagnostic ofsensor data in one embodiment. Although reference analyte values canuseful for checking and calibrating sensor data, self-diagnosticcapabilities of the sensor provide for a fail-safe for displaying sensordata with confidence and enable minimal user interaction (for example,requiring reference analyte values only as needed).

At block 582, a sensor data receiving module, also referred to as thesensor data module, receives new sensor data from the sensor.

At block 584, the sensor data transformation module continuously (orintermittently) converts received new sensor data into estimated analytevalues, also referred to as calibrated data.

At block 586, a self-diagnostics module, also referred to as a fail-safemodule, performs one or more calculations to determine the accuracy,reliability, and/or clinical acceptability of the sensor data. Someexamples of the self-diagnostics module are described above, withreference block 556. The self-diagnostics module can be furtherconfigured to run periodically (e.g., intermittently or in response to atrigger), for example, on raw data, filtered data, calibrated data,predicted data, and the like.

In certain embodiments, the self-diagnostics module evaluates an amountof time since sensor insertion into the host, wherein a threshold is setfor the sensor's usable life, after which time period the sensor isconsidered to be unreliable. In certain embodiments, theself-diagnostics module counts the number of times a failure or reset isrequired (for example, how many times the system is forced intosuspended or start-up mode), wherein a count threshold is set for apredetermined time period, above which the system is considered to beunreliable. In certain embodiments, the self-diagnostics module comparesnewly received calibrated sensor data with previously calibrated sensordata for aberrant values, such as is described in more detail elsewhereherein. In certain embodiments, the self-diagnostics module evaluatesclinical acceptability, such as is described in more detail withreference to FIG. 28, above. In certain embodiments, diagnostics, suchas are described in U.S. Publication No. US-2005-0161346-A1 and U.S.Publication No. US-2005-0143635-A1, can be incorporated into the systemsof preferred embodiments for system diagnosis, for example, foridentifying interfering species on the sensor signal and for identifyingdrifts in baseline and sensitivity of the sensor signal.

At block 588, a mode determination module, which can be a part of thesensor evaluation module 524, determines in which mode the sensor shouldbe set (or remain). In some embodiments, the system is programmed withthree modes: 1) start-up mode; 2) normal mode; and 3) suspended mode.Although three modes are described herein, the preferred embodiments arelimited to the number or types of modes with which the system can beprogrammed. In some embodiments, the system is defined as “in-cal” (incalibration) in normal mode; otherwise, the system is defined as“out-of-cal” (out of calibration) in start-up and suspended mode. Theterms as used herein are meant to describe the functionality and are notlimiting in their definitions.

Preferably, a start-up mode is provided, wherein the start-up mode isset when the system determines that it can no longer remain in suspendedor normal mode (for example, due to problems detected by theself-diagnostics module, such as described in more detail above) and/orwherein the system is notified that a new sensor has been inserted. Uponinitialization of start-up mode, the system ensures that any old matcheddata pairs and/or calibration information is purged. In start-up mode,the system initializes the calibration set, such as described in moredetail with reference to U.S. Publication No. 2006-0036142-A1. Once thecalibration set has been initialized, sensor data is ready forconversion and the system is set to normal mode.

Preferably, a normal mode is provided, wherein the normal mode is setwhen the system is accurately and reliably converting sensor data, forexample, wherein clinical acceptability is positively determined,aberrant values are negatively determined, and/or the self-diagnosticsmodules confirms reliability of data. In normal mode, the systemcontinuously (or intermittently) converts (calibrates) sensor data.Additionally, reference analyte values received by the system arematched with sensor data points and added to the calibration set.

In certain embodiments, the calibration set is limited to apredetermined number of matched data pairs, after which the systemspurges old or less desirable matched data pairs when a new matched datapair is added to the calibration set. Less desirable matched data pairscan be determined by inclusion criteria, which include one or morecriteria that define a set of matched data pairs that form asubstantially optimal calibration set.

One inclusion criterion comprises ensuring the time stamp of the matcheddata pairs (that make up the calibration set) span at least apreselected time period (e.g., three hours). Another inclusion criterioncomprises ensuring that the time stamps of the matched data pairs arenot more than a preselected age (e.g., one week old). Another inclusioncriterion ensures that the matched pairs of the calibration set have asubstantially evenly distributed amount of high and low raw sensor datapoints, estimated sensor analyte values, and/or reference analytevalues. Another criterion comprises ensuring all raw sensor data,estimated sensor analyte values, and/or reference analyte values arewithin a predetermined range (e.g., 40 mg/dL to 400 mg/dL for glucosevalues). Another criterion comprises evaluating the rate of change ofthe analyte concentration (e.g., from sensor data) during the time stampof the matched pair(s). For example, sensor and reference data obtainedduring the time when the analyte concentration is undergoing a slow rateof change can be less susceptible to inaccuracies caused by time lag andother physiological and non-physiological effects. Another criterioncomprises evaluating the congruence of respective sensor and referencedata in each matched data pair; the matched pairs with the mostcongruence can be chosen. Another criterion comprises evaluatingphysiological changes (e.g., low oxygen due to a user's posture,position, or motion that can cause pressure on the sensor and effect thefunction of a subcutaneously implantable analyte sensor, or othereffects) to ascertain a likelihood of error in the sensor value.Evaluation of calibration set criteria can comprise evaluating one,some, or all of the above described inclusion criteria. It iscontemplated that additional embodiments can comprise additionalinclusion criteria not explicitly described herein.

Unfortunately, some circumstances can exist wherein a system in normalmode can be changed to start-up or suspended mode. In general, thesystem is programmed to change to suspended mode when a failure ofclinical acceptability, aberrant value check and/or otherself-diagnostic evaluation is determined, such as described in moredetail above, and wherein the system requires further processing todetermine whether a system re-start is required (e.g., start-up mode).In general, the system will change to start-up mode when the system isunable to resolve itself in suspended mode and/or when the systemdetects a new sensor has been inserted (e.g., via system trigger or userinput).

Preferably, a suspended mode is provided wherein the suspended mode isset when a failure of clinical acceptability, aberrant value check,and/or other self-diagnostic evaluation determines unreliability ofsensor data. In certain embodiments, the system enters suspended modewhen a predetermined time period passes without receiving a referenceanalyte value. In suspended mode, the calibration set is not updatedwith new matched data pairs, and sensor data can optionally beconverted, but not displayed on the user interface. The system can bechanged to normal mode upon resolution of a problem (positive evaluationof sensor reliability from the self-diagnostics module, for example).The system can be changed to start-up mode when the system is unable toresolve itself in suspended mode and/or when the system detects a newsensor has been inserted (via system trigger or user input).

The systems of preferred embodiments, including a transcutaneous analytesensor, mounting unit, electronics unit, applicator, and receiver forinserting the sensor, and measuring, processing, and displaying sensordata, provide improved convenience and accuracy because of theirdesigned stability within the host's tissue with minimum invasivetrauma, while providing a discreet and reliable data processing anddisplay, thereby increasing overall host comfort, confidence, safety,and convenience. Namely, the geometric configuration, sizing, andmaterial of the sensor of the preferred embodiments enable themanufacture and use of an atraumatic device for continuous measurementof analytes, in contrast to conventional continuous glucose sensorsavailable to persons with diabetes, for example. Additionally, thesensor systems of preferred embodiments provide a comfortable andreliable system for inserting a sensor and measuring an analyte levelfor up to 7 days or more without surgery. The sensor systems of thepreferred embodiments are designed for host comfort, with chemical andmechanical stability that provides measurement accuracy. Furthermore,the mounting unit is designed with a miniaturized and reusableelectronics unit that maintains a low profile during use. The usablelife of the sensor can be extended by incorporation of a bioactive agentinto the sensor that provides local release of an anti-inflammatory, forexample, in order to slow the subcutaneous foreign body response to thesensor.

After the usable life of the sensor (for example, due to a predeterminedexpiration, potential infection, or level of inflammation), the host canremove the sensor and mounting from the skin, and dispose of the sensorand mounting unit (preferably saving the electronics unit for reuse).Another sensor system can be inserted with the reusable electronics unitand thus provide continuous sensor output for long periods of time.

FIG. 30 is a flow chart 600 that illustrates the process of detectingand processing signal artifacts in some embodiments.

At block 602, a sensor data receiving module, also referred to as thesensor data module, or processor module, receives sensor data (e.g., adata stream), including one or more time-spaced sensor data points. Insome embodiments, the data stream is stored in the sensor for additionalprocessing; in some alternative embodiments, the sensor periodicallytransmits the data stream to the receiver, which can be in wired orwireless communication with the sensor. In some embodiments, raw and/orfiltered data is stored in the sensor and/or transmitted and stored inthe receiver, as described in more detail elsewhere herein.

At block 604, a signal artifacts detection module, also referred to asthe signal artifacts detector, or signal reliability module, isprogrammed to detect transient non-glucose related signal artifacts inthe data stream, In some embodiments, the signal artifacts detector cancomprise an oxygen detector, a pH detector, a temperature detector,and/or a pressure/stress detector, for example, the signal artifactsdetector 29 in FIG. 2. In some embodiments, the signal artifactsdetector is located within the processor 22 (FIG. 2) and utilizesexisting components of the glucose sensor to detect signal artifacts,for example by pulsed amperometric detection, counter electrodemonitoring, reference electrode monitoring, and frequency contentmonitoring, which are described elsewhere herein. In yet otherembodiments, the data can be sent from the sensor to the receiver whichcomprises programming in the processor 42 (FIG. 4) that performsalgorithms to detect signal artifacts, for example such as describedwith reference to “Cone of Possibility Detection” method and/or bycomparing raw data vs. filtered data, both of which are described inmore detail elsewhere herein.

In some exemplary embodiments, the processor module in either the sensorelectronics and/or the receiver electronics evaluates an intermittent orcontinuous signal-to-noise measurement to determine aberrancy of sensordata responsive to a signal-to-noise ratio above a set threshold. Insome exemplary embodiments, signal residuals (e.g., by comparing raw andfiltered data) are intermittently or continuously analyzed for noiseabove a set threshold. In some exemplary embodiments, patternrecognition can be used to identify noise associated with physiologicalconditions, such as low oxygen, or other known signal aberrancies.Accordingly, in these embodiments, the system can be configured, inresponse to aberrancies in the data stream, to trigger signalestimation, adaptively filter the data stream according to theaberrancy, and the like, as described in more detail elsewhere herein.

In some embodiments, one or more signal residuals are obtained bycomparing received data with filtered data, whereby a signal artifactcan be determined. In some embodiments, a signal artifact event isdetermined to have occurred if the residual is greater than a threshold.In some exemplary embodiments, another signal artifact event isdetermined to have occurred if the residual is greater than a secondthreshold. In some exemplary embodiments, a signal artifact event isdetermined to have occurred if the residual is greater than a thresholdfor a period of time or amount of data. In some exemplary embodiments, asignal artifact event is determined to have occurred if a predeterminednumber of signal residuals above a predetermined threshold occur withina predetermined time period (or amount of data). In some exemplaryembodiments, an average of a plurality of residuals is evaluated over aperiod of time or amount of data to determine whether a signal artifacthas occurred. The use of residuals for noise detection can be preferredin circumstances where data gaps (non-continuous) data exists.

In some exemplary embodiments, a differential, also referred to as aderivative of the residual, is determined by comparing a first residual(e.g., at a first time point) and a second residual (e.g., at a secondtime point), wherein a signal artifact event is determined to haveoccurred when the differential is above a predetermined threshold. Insome exemplary embodiments, a signal artifact event is determined tohave occurred if the differential is greater than a threshold for aperiod of time or amount of data. In some exemplary embodiments, anaverage of a plurality of differentials is calculated over a period oftime or amount of data to determine whether a signal artifact hasoccurred.

Numerous embodiments for detecting signal artifacts are described inmore detail in the section entitled, “Signal Artifacts Detection,” allof which are encompassed by the signal artifacts detection at block 604.

At block 606, the processor module is configured to process the sensordata based at least in part on whether the signal artifact event hasoccurred.

In some embodiments, the sensor data is filtered in the receiverprocessor to generate filtered data if the signal artifact event isdetermined to have occurred; filtering can be performed either on theraw data, or can be performed to further filter received filtered data,or both.

In some embodiments, signal artifacts detection and processing isutilized in outlier detection, such as described in more detailelsewhere herein, wherein a disagreement between time correspondingreference data and sensor data can be analyzed, e.g., noise analysisdata (e.g., signal artifacts detection and signal processing) can beused to determine which value is likely more reliable (e.g., whether thesensor data and/or reference data can be used for processing). In someexemplary embodiments wherein the processor module receives referencedata from a reference analyte monitor, a reliability of the receiveddata is determined based on signal artifacts detection (e.g., if asignal artifact event is determined to have occurred.) In some exemplaryembodiments, a reliability of the sensor data is determined based onsignal artifacts detection (e.g., if the signal artifact event isdetermined to have not occurred.) The term “reliability,” as usedherein, is a broad term and is used in its ordinary sense, including,without limitation, a level of confidence in the data (e.g., sensor orreference data), for example, a positive or negative reliance on thedata (e.g., for calibration, display, and the like) and/or a rating(e.g., of at least 60%, 70%, 80%, 90% or 100% confidence thereon.)

In some embodiments wherein a matching data pair is formed by matchingreference data to substantially time corresponding sensor data (e.g.,for calibration and/or outlier detection) described in more detailelsewhere herein, matching of a data pair can be configured to occurbased on signal artifacts detection (e.g., only if a signal artifactevent is determined to have not occurred.) In some embodiments whereinthe reference data is included in a calibration factor for use incalibration of the glucose sensor as described in more detail elsewhereherein, the reference data can be configured to be included based onsignal artifacts detection (e.g., only if the signal artifact event isdetermined to have not occurred.) In general, results of noise analysis(e.g., signal artifact detection and/or signal processing) can be usedto determine when to use or eliminate a matched pair for use incalibration (e.g., calibration set).

In some embodiments, a user is prompted for a reference glucose valuebased on signal artifacts detection (e.g., only if a signal artifactevent is determined to have not occurred.) While not wishing to be boundby theory, it is believed certain more preferable times for calibration(e.g., not during noise episodes) can be detected and processed byprompting the user for calibration during those times.

In some embodiments, results of noise analysis (e.g., signal artifactdetection and/or signal processing) can be used to determine how toprocess the sensor data. For example, different levels of signalprocessing and display (e.g., raw data, integrated data, filtered datautilizing a first filter, filtered data utilizing a second filter, whichmay be “more aggressive” than the first filter by filtering over alarger time period, and the like.) Accordingly, the different levels ofsignal processing and display can be selectively chosen responsive to areliability measurement, a positive or negative determination of signalartifact, and/or signal artifacts above first and second predeterminedthresholds.

In some embodiments, results of noise analysis (e.g., signal artifactdetection and/or signal processing) can be used to determine when toutilize and/or display different representations of the sensor data(e.g., raw vs. filtered data), when to turn filters on and/or off (e.g.,processing and/or display of certain smoothing algorithms), and/or whento further process the sensor data (e.g., filtering and/or displaying).In some embodiments, the display of the sensor data is dependent uponthe determination of signal artifact(s). For example, when a certainpredetermined threshold of signal artifacts have been detected (e.g.,noisy sensor data), the system is configured to modify or turn off aparticular display of the sensor data (e.g., display filtered data,display processed data, disable display of sensor data, display range ofpossible data values, display indication of direction of glucose trenddata, replace sensor data with predicted/estimated sensor data, and/ordisplay confidence interval representative of a level of confidence inthe sensor data.) In some exemplary embodiments, a graphicalrepresentation of filtered sensor data is displayed if the signalartifact event is determined to have occurred. Alternatively, when acertain predetermined threshold of signal artifacts has not beendetected (e.g., minimal, insignificant, or no noise in the data signal),the system is configured to modify or turn on a particular display ofthe sensor data (e.g., display unfiltered (e.g., raw or integrated)data, a single data value, an indication of direction of glucose trenddata, predicted glucose data for a future time period and/or aconfidence interval representative of a level of confidence in thesensor data.)

In some embodiments wherein a residual (or differential) is determinedas described in more detail elsewhere herein, the residual (ordifferential) is used to modify the filtered data during signal artifactevent(s). In one such exemplary embodiment, the residual is measured andthen added back into the filtered signal. While not wishing to be boundby theory, it is believed that some smoothing algorithms may result insome loss of dynamic behavior representative of the glucoseconcentration, which disadvantage may be reduced or eliminated by theadding of the residual back into the filtered signal in somecircumstances.

In some embodiments, the sensor data can be modified to compensate for atime lag, for example by predicting or estimating an actual glucoseconcentration for a time period considering a time lag associated withdiffusion of the glucose through the membrane, digital signalprocessing, and/or algorithmically induced time lag, for example.

FIG. 31 is a graph that illustrates a raw data stream from a glucosesensor for approximately 24 hours with a filtered version of the samedata stream superimposed on the same graph. Additionally, this graphillustrates a noise episode, the beginning and end of which was detectedby a noise detection algorithm of the preferred embodiments, and duringwhich a particular filter was applied to the data. The x-axis representstime in minutes; the y-axis represents the raw and filtered data valuesin counts. In this example, the raw data stream was obtained in 5 minuteintervals from a transcutaneous glucose sensor such as described in moredetail above, with reference to FIG. 1B and in U.S. Publication No.US-2006-00201087-A1.

In section 608 of the data, which encompasses an approximately 14 hourperiod up to time=2:22, the filtered data was obtained by applying a3-point moving average window to the raw data. During that period, thenoise detection algorithm was applied to detect a noise episode. In thisexample, the algorithm included the following: calculating a residualsignal by subtracting the filtered data from the raw data (e.g., foreach 5-minute point); calculating a differential by subtracting theresidual for each 5-minute point from its previous 5-minute residual;determining if each differential exceeds a threshold of 5000 counts (anddeclaring a noisy point if so); and determining whether 6 out of 12points in the past 1 hour exceed that threshold (and declaring a noiseepisode if so). Accordingly, a noise episode was declared at time=2:22and a more aggressive filter was applied as described with reference tosection 610.

In section 610 of the data, also referred to as a noise episode, whichencompasses an approximately 5½ hour period up to time=7:57, thefiltered data was obtained by applying a 7-point moving average windowto the raw data. The 7-point moving average window was in this examplewas an effective filter in smoothing out the noise in the data signal ascan be seen on the graph. During that period, an algorithm was appliedto detect when the noise episode had ended. In this example, thealgorithm included the following: calculating a residual signal bysubtracting the filtered data (using the 3-point moving average filterdescribed above) from the raw data (e.g., for each 5-minute point);calculating a differential of the residual by subtracting the residualfor each 5-minute point from its previous 5-minute residual; determiningif each differential exceeds a threshold of 5000 counts (and declaring anoisy point if so); and determining whether less than 2 noisy points hadoccurred in the past hour (and declaring the noise episode over if so).Accordingly, the noise episode was declared as over at time-7:57 and theless aggressive filter (e.g., 3-point moving average) was again appliedwith the noise detection algorithm as described with reference tosection 608, above.

In section 612 of the data, which encompasses more than 4 hours of data,the filtered data was obtained by applying a 3-point moving averagewindow to the raw data. During that period, the noise detectionalgorithm (described above) did not detect a noise episode. Accordingly,raw or minimally filtered data could be displayed to the patient duringthis time period.

It was shown that the above-described example provided smoother glucoseinformation during noise episodes, by applying a more aggressive filterto smooth out the noise. It is believed that when displayed, thesmoother data will avoid presenting potentially misleading or inaccurateinformation to the user. Additionally, it was shown in theabove-described example that during non-noisy periods (when noiseepisodes are not detected), raw or less aggressively filtered data canbe displayed to the user in order to provide more accurate data withminimal or no associated filter-induced time lag in the data.Furthermore, it is believed that proper detection of noise episodes aidsin determining proper times for calibration, ensuring more accuratecalibration than may otherwise be possible.

In the above-described example, the criteria for the onset & offset ofnoise episodes were different; for example, the onset criteria included6 out of 12 points in the past 1 hour exceeding a threshold, while theoffset criteria included less than 2 noisy points in the past 1 hour. Inthis example, these different criteria were found to create smoothertransitions in the data between the raw and filtered data and avoidedfalse detections of noise episodes.

FIG. 32 is a flowchart 700 that illustrates a method for processing datafrom a glucose sensor in certain embodiments. In general, prior artsystems display either real-time sensor data (e.g., prospectivelycalibrated/analyzed) or historical sensor data (e.g., retrospectivelycalibrated/analyzed). Regarding real-time sensor data display, thesensor data is typically prospectively processed (e.g., calibrated,smoothed, etc) in substantially real-time by a predetermined algorithm,wherein the real-time prospectively processed data are displayedperiodically or substantially continuously based on that prospectiveanalysis. Regarding historical sensor data display, the sensor data istypically retrospectively processed (e.g., calibrated, smoothed, etc)after collection of an entire sensor data set, wherein the historicalretrospectively processed data are displayed based on the retrospectiveanalysis.

In contrast to the prior art, the preferred embodiments describe systemsand methods for periodically or substantially continuouslypost-processing (e.g., updating) the substantially real-time graphicalrepresentation of glucose data (e.g., trend graph representative ofglucose concentration over a previous number of minutes or hours) withprocessed data, wherein the data has been processed responsive todetection of signal artifacts.

At block 702, a sensor data receiving module, also referred to as thesensor data module, or processor module, receives sensor data (e.g., adata stream), including one or more time-spaced sensor data points. Insome embodiments, the data stream is stored in the sensor for additionalprocessing; in some alternative embodiments, the sensor periodicallytransmits the data stream to the receiver, which can be in wired orwireless communication with the sensor. In some embodiments, raw and/orfiltered data is stored in the sensor and/or transmitted and stored inthe receiver, as described in more detail elsewhere herein.

At block 704, a signal artifacts detection module, also referred to asthe signal artifacts detector, or signal reliability module, optionallydetects transient non-glucose related signal artifacts in the datastream, such as described in more detail above with reference to block604.

At block 706, the processor module is configured to optionally processthe sensor data based at least in part on whether the signal artifactevent has occurred, such as described in more detail with reference toblock 606 above.

At block 708, an output module, also referred to as the processormodule, provides output to the user via the user interface. The outputis representative of the estimated glucose value, which is determined byconverting the sensor data into a meaningful glucose value such asdescribed in more detail elsewhere herein. User output can be in theform of a numeric estimated glucose value, an indication of directionaltrend of glucose concentration, and/or a graphical representation of theestimated glucose data over a period of time, for example. Otherrepresentations of the estimated glucose values are also possible, forexample audio and tactile. In some embodiments, the output moduledisplays both a “real-time”glucose value (e.g., a number representativeof the most recently measure glucose concentration) and a graphicalrepresentation of the post-processed sensor data, which is described inmore detail, below.

In one embodiment, such as shown in FIG. 3A, the estimated glucose valueis represented by a numeric value. In other exemplary embodiments, suchas shown in FIGS. 3B to 3D, the user interface graphically representsthe estimated glucose data trend over predetermined a time period (e.g.,one, three, and nine hours, respectively). In alternative embodiments,other time periods can be represented. In some embodiments, the measuredanalyte value is represented by a numeric value. In alternativeembodiments, other time periods can be represented. In alternativeembodiments, pictures, animation, charts, graphs, ranges of values, andnumeric data can be selectively displayed.

At block 710, the processor module is configured to periodically orsubstantially continuously post-process (e.g., update) the displayedgraphical representation of the data corresponding to the time periodaccording to the received data. For example, the glucose trendinformation (e.g., for the previous 1-, 3-, or 9-hour trend graphs shownin FIGS. 3B to 3D) can be updated to better represent actual glucosevalues during signal artifacts. In some embodiments, the processormodule post-processes segments of data (e.g., 1-, 3-, or 9-hour trendgraph data) every few seconds, minutes, hours, days, or anywhere inbetween, and/or when requested by a user (e.g., in responsive to abutton-activation such as a request for display of a 3-hour trend graphscreen).

In general, post-processing includes the processing performed by theprocessor module (e.g., within the hand-held receiver unit) on “recent”sensor data (e.g., data that is inclusive of time points within the pastfew minutes to few hours) after its initial display of the sensor dataand prior to what is generally termed “retrospective analysis” in theart (e.g., analysis that is typically accomplished retrospectively atone time, in contrast to intermittently, periodically, or continuously,on an entire data set, such as for display of sensor data for physiciananalysis). Post-processing can include programming performed torecalibrate the sensor data (e.g., to better match to reference values),fill in data gaps (e.g., data eliminated due to noise or otherproblems), smooth out (filter) sensor data, compensate for a time lag inthe sensor data, and the like, which is described in more detail, below.Preferably, the post-processed data is displayed on a personal hand-heldunit (e.g., such as on the 1-, 3-, and 9-hour trend graphs of thereceiver of FIGS. 3A to 3D) in “real time” (e.g., inclusive of recentdata within the past few minutes or hours) and can be updated(post-processed) automatically (e.g., periodically, intermittently, orcontinuously) or selectively (e.g., responsive to a request) when new oradditional information is available (e.g., new reference data, newsensor data, etc). In some alternative embodiments, post-processing canbe triggered dependent upon the duration of a noise episode; forexample, data associated with noise events extending past about 30minutes can be processed and/or displayed differently than data duringthe initial 30 minutes of a noise episode.

In one exemplary embodiment, the processor module filters the datastream to recalculate data for a previous time period and periodicallyor substantially continuously displays a graphical representation of therecalculated data for that time period (e.g., trend graph).

In another exemplary embodiment, the processor module adjusts the datafor a time lag (e.g., removes a time lag induced by real-time filtering)from data for a previous time period and displays a graphicalrepresentation of the time lag adjusted data for that time period (e.g.,trend graph).

In another exemplary embodiment, the processor module algorithmicallysmoothes one or more sensor data points over a moving window (e.g.,including time points before and after the one or more sensor datapoints) for data for a previous time period and displays a graphicalrepresentation of the updated, smoothed data for that time period (e.g.,trend graph).

Although a few examples of post-processing are described herein, oneskilled in the art appreciates a variety of data processing that can beapplied to these systems and methods, including any of the processingsteps described in more detail elsewhere herein.

Methods and devices that are suitable for use in conjunction withaspects of the preferred embodiments are disclosed in U.S. Pat. Nos.4,994,167; 4,757,022; 6,001,067; 6,741,877; 6,702,857; 6,558,321;6,931,327; and 6,862,465.

Methods and devices that are suitable for use in conjunction withaspects of the preferred embodiments are disclosed in U.S. PublicationNo. US-2005-0176136-A1; U.S. Publication No. US-2005-0251083-A1; U.S.Publication No. US-2005-0143635-A1; U.S. Publication No.US-2005-0181012-A1; U.S. Publication No. US-2005-0177036-A1; U.S.Publication No. US-2005-0124873-A1; U.S. Publication No.US-2005-0051440-A1; U.S. Publication No. US-2005-0115832-A1; U.S.Publication No. US-2005-0245799-A1; U.S. Publication No.US-2005-0245795-A1; U.S. Publication No. US-2005-0242479-A1; U.S.Publication No. US-2005-0182451-A1; U.S. Publication No.US-2005-0056552-A1; U.S. Publication No. US-2005-0192557-A1; U.S.Publication No. US-2005-0154271-A1; U.S. Publication No.US-2004-0199059-A1; U.S. Publication No. US-2005-0054909-A1; U.S.Publication No. US-2005-0112169-A1; U.S. Publication No.US-2005-0051427-A1; U.S. Publication No. US-2003-0032874-A1; U.S.Publication No. US-2005-0103625-A1; U.S. Publication No.US-2005-0203360-A1; U.S. Publication No. US-2005-0090607-A1; U.S.Publication No. US-2005-0187720-A1; U.S. Publication No.US-2005-0161346-A1; U.S. Publication No. US-2006-0015020-A1; U.S.Publication No. US-2005-0043598-A1; U.S. Publication No.US-2003-0217966-A1; U.S. Publication No. US-2005-0033132-A1; U.S.Publication No. US-2005-0031689-A1; U.S. Publication No.US-2004-0045879-A1; U.S. Publication No. US-2004-0186362-A1; U.S.Publication No. US-2005-0027463-A1; U.S. Publication No.US-2005-0027181-A1; U.S. Publication No. US-2005-0027180-A1; U.S.Publication No. US-2006-0020187-A1; U.S. Publication No.US-2006-0036142-A1; U.S. Publication No. US-2006-0020192-A1; U.S.Publication No. US-2006-0036143-A1; U.S. Publication No.US-2006-0036140-A1; U.S. Publication No. US-2006-0019327-A1; U.S.Publication No. US-2006-0020186-A1; U.S. Publication No.US-2006-0020189-A1; U.S. Publication No. US-2006-0036139-A1; U.S.Publication No. US-2006-0020191-A1; U.S. Publication No.US-2006-0020188-A1; U.S. Publication No. US-2006-0036141-A1; U.S.Publication No. US-2006-0020190-A1; U.S. Publication No.US-2006-0036145-A1; U.S. Publication No. US-2006-0036144-A1; U.S.Publication No. US-2006-0016700-A1; U.S. Publication No.US-2006-0142651-A1; U.S. Publication No. US-2006-0086624-A1; U.S.Publication No. US-2006-0068208-A1; U.S. Publication No.US-2006-0040402-A1; U.S. Publication No. US-2006-0036142-A1; U.S.Publication No. US-2006-0036141-A1; U.S. Publication No.US-2006-0036143-A1; U.S. Publication No. US-2006-0036140-A1; U.S.Publication No. US-2006-0036139-A1; U.S. Publication No.US-2006-0142651-A1; U.S. Publication No. US-2006-0036145-A1; and U.S.Publication No. US-2006-0036144-A1.

Methods and devices that are suitable for use in conjunction withaspects of the preferred embodiments are disclosed in U.S. applicationSer. No. 09/447,227 filed Nov. 22, 1999 and entitled “DEVICE AND METHODFOR DETERMINING ANALYTE LEVELS”; U.S. application Ser. No. 11/335,879filed Jan. 18, 2006 and entitled “CELLULOSIC-BASED INTERFERENCE DOMAINFOR AN ANALYTE SENSOR”; U.S. application Ser. No. 11/334,876 filed Jan.18, 2006 and entitled “TRANSCUTANEOUS ANALYTE SENSOR”; U.S. applicationSer. No. 11/333,837 filed Jan. 17, 2006 and entitled “LOW OXYGEN IN VIVOANALYTE SENSOR”.

All references cited herein, including but not limited to published andunpublished applications, patents, and literature references, areincorporated herein by reference in their entirety and are hereby made apart of this specification. To the extent publications and patents orpatent applications incorporated by reference contradict the disclosurecontained in the specification, the specification is intended tosupersede and/or take precedence over any such contradictory material.

The term “comprising” as used herein is synonymous with “including,”“containing,” or “characterized by,” and is inclusive or open-ended anddoes not exclude additional, unrecited elements or method steps.

All numbers expressing quantities of ingredients, reaction conditions,and so forth used in the specification are to be understood as beingmodified in all instances by the term “about.” Accordingly, unlessindicated to the contrary, the numerical parameters set forth herein areapproximations that may vary depending upon the desired propertiessought to be obtained. At the very least, and not as an attempt to limitthe application of the doctrine of equivalents to the scope of anyclaims in any application claiming priority to the present application,each numerical parameter should be construed in light of the number ofsignificant digits and ordinary rounding approaches.

The above description discloses several methods and materials of thepresent invention. This invention is susceptible to modifications in themethods and materials, as well as alterations in the fabrication methodsand equipment. Such modifications will become apparent to those skilledin the art from a consideration of this disclosure or practice of theinvention disclosed herein. Consequently, it is not intended that thisinvention be limited to the specific embodiments disclosed herein, butthat it cover all modifications and alternatives coming within the truescope and spirit of the invention.

What is claimed is:
 1. A method for processing sensor data from acontinuous glucose sensor, the method comprising: receiving continuousglucose sensor data, the continuous glucose sensor data; determining,using processing electronics, a level of reliability associated with thecontinuous glucose sensor data, wherein the level of reliability isselected from three or more predetermined levels of reliability; andprocessing the sensor data differently depending upon the determinedlevel of reliability.
 2. The method of claim 1, wherein the processingcomprises displaying a representation of filtered continuous glucosesensor data.
 3. The method of claim 2, wherein the filtered continuousglucose sensor data is post-processed, and wherein post-processingcomprises filtering the data to recalculate data and displaying agraphical representation of the recalculated data corresponding to thetime period.
 4. The method of claim 1, wherein the processing comprisesoutputting a range of possible data values.
 5. The method of claim 1,wherein the processing comprises outputting an indication of directionof glucose trend data.
 6. The method of claim 1, wherein the processingcomprises outputting insulin delivery information.
 7. The method ofclaim 1, wherein the processing comprises prompting the user to providea reference data point for calibration.
 8. A method for processingsensor data from a continuous glucose sensor, the method comprising:receiving continuous glucose sensor data, the continuous glucose sensordata; determining, using processing electronics, a level of reliabilityassociated with the continuous glucose sensor data, wherein the level ofreliability is selected from three or more predetermined levels ofreliability; and processing the sensor data differently depending uponthe determined level of reliability, wherein determining a level ofreliability comprises performing at least one of a first derivativeanalysis or a second derivative analysis of the continuous glucosesensor data for a time period.
 9. A method for processing sensor datafrom a continuous glucose sensor, the method comprising: receivingcontinuous glucose sensor data, the continuous glucose sensor data;determining, using processing electronics, a level of reliabilityassociated with the continuous glucose sensor data, wherein the level ofreliability is selected from three or more predetermined levels ofreliability; and processing the sensor data differently depending uponthe determined level of reliability, wherein determining a level ofreliability comprises comparing a raw version of the sensor data with afiltered version of the sensor data to obtain at least one residual andcomparing the at least one residual to at least one threshold.
 10. Amethod for processing sensor data from a continuous glucose sensor, themethod comprising: receiving continuous glucose sensor data, thecontinuous glucose sensor data; determining, using processingelectronics, a level of reliability associated with the continuousglucose sensor data, wherein the level of reliability is selected fromthree or more predetermined levels of reliability; and processing thesensor data differently depending upon the determined level ofreliability, wherein the processing comprises determining when toutilize and/or display different representations of the sensor data,wherein the different representations are selected from a plurality ofpredetermined different representations of the sensor data.
 11. A methodfor processing sensor data from a continuous glucose sensor, the methodcomprising: receiving continuous glucose sensor data, the continuousglucose sensor data; determining, using processing electronics, a levelof reliability associated with the continuous glucose sensor data,wherein the level of reliability is selected from three or morepredetermined levels of reliability; and processing the sensor datadifferently depending upon the determined level of reliability, whereinthe processing comprises determining when to when to utilize differentfilters.
 12. A system for processing sensor data from a continuousglucose sensor, the system comprising: a continuous glucose sensorconfigured to generate sensor data; processing electronics operablyconnected to the continuous glucose sensor, wherein the processingelectronics are configured to determine a level of reliability of thesensor data and to process the sensor data differently depending uponthe determined level of reliability, wherein determining the level ofreliability includes selecting the level of reliability from three ormore predetermined levels of reliability.
 13. The system of claim 12,wherein the processing electronics are configured to post-process thedata, and wherein the output module is configured to selectively displaya graphical representation of the post-processed data based on the levelof reliability.
 14. The system of claim 12, wherein the processingelectronics are configured to process the sensor data by outputting arange of possible data values.
 15. The system of claim 12, wherein theprocessing electronics configured to process the sensor data byoutputting an indication of direction of glucose trend data.
 16. Thesystem of claim 12, wherein the processing electronics configured toprocess the sensor data by outputting insulin delivery information. 17.The system of claim 12, wherein the processing electronics configured toprocess the sensor data by prompting the user to provide a referencedata point for calibration.
 18. A system for processing sensor data froma continuous glucose sensor, the system comprising: a continuous glucosesensor configured to generate sensor data; processing electronicsoperably connected to the continuous glucose sensor, wherein theprocessing electronics are configured to determine a level ofreliability of the sensor data and to process the sensor datadifferently depending upon the determined level of reliability, whereindetermining the level of reliability includes selecting the level ofreliability from three or more predetermined levels of reliability,wherein the processing electronics are configured to determine the levelof reliability based one at least one of a first derivative analysis ora second derivative analysis of the continuous glucose sensor data for atime period.
 19. A system for processing sensor data from a continuousglucose sensor, the system comprising: a continuous glucose sensorconfigured to generate sensor data; processing electronics operablyconnected to the continuous glucose sensor, wherein the processingelectronics are configured to determine a level of reliability of thesensor data and to process the sensor data differently depending uponthe determined level of reliability, wherein determining the level ofreliability includes selecting the level of reliability from three ormore predetermined levels of reliability, wherein the processingelectronics are configured to determine the level of reliability basedon a comparison of a signal residual with a threshold, and wherein thesignal residual is based on a comparison of raw and filtered versions ofthe sensor data.
 20. A system for processing sensor data from acontinuous glucose sensor, the system comprising: a continuous glucosesensor configured to generate sensor data; processing electronicsoperably connected to the continuous glucose sensor, wherein theprocessing electronics are configured to determine a level ofreliability of the sensor data and to process the sensor datadifferently depending upon the determined level of reliability, whereindetermining the level of reliability includes selecting the level ofreliability from three or more predetermined levels of reliability,wherein the processing electronics are configured to process the sensordata by determining when to utilize and/or display differentrepresentations of the sensor data, wherein the differentrepresentations are selected from a plurality of predetermined differentrepresentations of the sensor data.
 21. A system for processing sensordata from a continuous glucose sensor, the system comprising: acontinuous glucose sensor configured to generate sensor data; processingelectronics operably connected to the continuous glucose sensor, whereinthe processing electronics are configured to determine a level ofreliability of the sensor data and to process the sensor datadifferently depending upon the determined level of reliability, whereindetermining the level of reliability includes selecting the level ofreliability from three or more predetermined levels of reliability,wherein the processing electronics are configured to process the sensordata by determining when to utilize different filters.